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 swaits
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swaits
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swaits
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a9edb0916f
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@@ -0,0 +1,40 @@
|
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---
|
||||
name: Bug report
|
||||
about: A correctness, performance, or panic bug in heuropt
|
||||
title: "bug: <one-line summary>"
|
||||
labels: bug
|
||||
---
|
||||
|
||||
## What happened
|
||||
|
||||
<Concise description of the bug.>
|
||||
|
||||
## Reproducer
|
||||
|
||||
```rust
|
||||
// Smallest example that demonstrates the bug. Ideally <30 lines and
|
||||
// runnable as a fresh `examples/repro.rs`. Include the Cargo.toml
|
||||
// `[features]` you used.
|
||||
```
|
||||
|
||||
Command used:
|
||||
|
||||
```sh
|
||||
cargo run --release --example repro
|
||||
```
|
||||
|
||||
## Expected vs observed
|
||||
|
||||
- **Expected:** <what should happen>
|
||||
- **Observed:** <what actually happens>
|
||||
|
||||
## Environment
|
||||
|
||||
- heuropt version:
|
||||
- `rustc --version`:
|
||||
- OS / arch:
|
||||
- Feature flags enabled:
|
||||
|
||||
## Additional context
|
||||
|
||||
<Anything else — fuzz artifact path, screenshots, profiler output.>
|
||||
@@ -0,0 +1,8 @@
|
||||
blank_issues_enabled: false
|
||||
contact_links:
|
||||
- name: Security vulnerability
|
||||
url: https://github.com/swaits/heuropt/security/advisories/new
|
||||
about: Please use private vulnerability reporting — do not open a public issue. See SECURITY.md.
|
||||
- name: Question / discussion
|
||||
url: https://github.com/swaits/heuropt/discussions
|
||||
about: For open-ended questions or design discussions.
|
||||
@@ -0,0 +1,24 @@
|
||||
---
|
||||
name: Docs issue
|
||||
about: Something in the README, mdbook guide, or rustdoc is wrong, missing, or unclear
|
||||
title: "docs: <one-line summary>"
|
||||
labels: documentation
|
||||
---
|
||||
|
||||
## Where
|
||||
|
||||
- [ ] `README.md`
|
||||
- [ ] mdbook user guide (chapter / section: ____ )
|
||||
- [ ] rustdoc on a specific item (path: ____ )
|
||||
- [ ] Examples (`examples/____.rs`)
|
||||
- [ ] CHANGELOG / migration guide
|
||||
- [ ] Other: ____
|
||||
|
||||
## What's wrong
|
||||
|
||||
<Concrete description: typo, broken link, outdated code sample,
|
||||
missing topic, unclear explanation, etc.>
|
||||
|
||||
## What it should say (if you know)
|
||||
|
||||
<Optional: proposed wording or correct content. Even a sketch helps.>
|
||||
@@ -0,0 +1,39 @@
|
||||
---
|
||||
name: Feature request
|
||||
about: Propose a new algorithm, operator, metric, or API addition
|
||||
title: "feat: <one-line summary>"
|
||||
labels: enhancement
|
||||
---
|
||||
|
||||
## What and why
|
||||
|
||||
<What you want, and the problem it solves. If this is a new algorithm
|
||||
or operator, cite the paper or canonical reference.>
|
||||
|
||||
## Proposed API sketch
|
||||
|
||||
```rust
|
||||
// What the public surface would look like — config struct fields,
|
||||
// trait impl, etc. Doesn't need to be final, just enough to discuss.
|
||||
```
|
||||
|
||||
## Alternatives considered
|
||||
|
||||
<Other approaches you thought about and why this one wins. If a
|
||||
similar feature already exists in heuropt or another Rust crate,
|
||||
explain how this differs.>
|
||||
|
||||
## Scope
|
||||
|
||||
- [ ] New trait (will need API discussion)
|
||||
- [ ] New algorithm
|
||||
- [ ] New operator
|
||||
- [ ] New metric / Pareto utility
|
||||
- [ ] New optional feature flag
|
||||
- [ ] Change to existing public API (potentially breaking)
|
||||
|
||||
## Willing to implement?
|
||||
|
||||
- [ ] Yes, I'll send a PR.
|
||||
- [ ] Yes, but I'd like guidance on the design first.
|
||||
- [ ] No, I'm reporting the need.
|
||||
@@ -0,0 +1,32 @@
|
||||
<!--
|
||||
Thanks for the contribution! Please skim CONTRIBUTING.md if you
|
||||
haven't yet — it has the local-test checklist and the conventional-
|
||||
commits requirement.
|
||||
-->
|
||||
|
||||
## What
|
||||
|
||||
<One- or two-sentence summary. Focus on the *what* and *why*, not
|
||||
the *how*.>
|
||||
|
||||
## Why
|
||||
|
||||
<Motivation. Link the issue this resolves with `Closes #N` if
|
||||
applicable.>
|
||||
|
||||
## Checklist
|
||||
|
||||
- [ ] `cargo fmt --all`
|
||||
- [ ] `cargo clippy --all-targets --all-features -- -D warnings`
|
||||
- [ ] `cargo test` and `cargo test --all-features`
|
||||
- [ ] `cargo doc --no-deps --all-features` (with `-D warnings`)
|
||||
- [ ] Conventional-commit subject(s) (`<type>(<scope>): <summary>`)
|
||||
- [ ] If touching algorithm output: confirmed bit-identical results
|
||||
via `cargo run --release --example compare`
|
||||
- [ ] If perf change: included gungraun before/after numbers in the
|
||||
commit message
|
||||
- [ ] Updated CHANGELOG.md under `[Unreleased]` if user-visible
|
||||
|
||||
## Anything else
|
||||
|
||||
<Caveats, follow-ups, screenshots, perf numbers, etc.>
|
||||
@@ -104,6 +104,10 @@ jobs:
|
||||
with:
|
||||
workspaces: fuzz -> target
|
||||
- name: Install cargo-fuzz
|
||||
run: cargo install cargo-fuzz --locked
|
||||
# No `--locked`: cargo-fuzz's bundled Cargo.lock pins
|
||||
# rustix=0.36.5, which uses the now-removed `rustc_attrs` cfg
|
||||
# name and fails to build on current nightly. Letting cargo
|
||||
# resolve fresh picks a recent rustix that builds cleanly.
|
||||
run: cargo install cargo-fuzz
|
||||
- name: 60-second soak
|
||||
run: cargo fuzz run ${{ matrix.target }} -- -max_total_time=60
|
||||
|
||||
@@ -0,0 +1,48 @@
|
||||
name: Docs
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [main]
|
||||
tags: ["v*.*.*"]
|
||||
workflow_dispatch:
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
pages: write
|
||||
id-token: write
|
||||
|
||||
concurrency:
|
||||
group: pages
|
||||
cancel-in-progress: false
|
||||
|
||||
jobs:
|
||||
build:
|
||||
name: Build mdbook
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- name: Install mdbook
|
||||
run: |
|
||||
mkdir -p ~/.local/bin
|
||||
curl -sSL "https://github.com/rust-lang/mdBook/releases/download/v0.4.40/mdbook-v0.4.40-x86_64-unknown-linux-musl.tar.gz" \
|
||||
| tar -xz -C ~/.local/bin
|
||||
echo "$HOME/.local/bin" >> "$GITHUB_PATH"
|
||||
- name: Build
|
||||
run: |
|
||||
cd docs/book
|
||||
mdbook build
|
||||
- uses: actions/configure-pages@v5
|
||||
- uses: actions/upload-pages-artifact@v3
|
||||
with:
|
||||
path: target/book
|
||||
|
||||
deploy:
|
||||
name: Deploy to GitHub Pages
|
||||
needs: build
|
||||
runs-on: ubuntu-latest
|
||||
environment:
|
||||
name: github-pages
|
||||
url: ${{ steps.deployment.outputs.page_url }}
|
||||
steps:
|
||||
- id: deployment
|
||||
uses: actions/deploy-pages@v4
|
||||
+160
-1
@@ -7,6 +7,165 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
|
||||
## [Unreleased]
|
||||
|
||||
## [0.6.0] — 2026-05-05
|
||||
|
||||
Theme: production lifecycle. heuropt becomes deployable for long-
|
||||
running, real-world optimization workloads — callbacks, stop
|
||||
conditions, tracing, and two new performance indicators.
|
||||
|
||||
No breaking changes to the public API. Existing `Optimizer<P>` impls
|
||||
keep compiling — `run_with` is added as a default-impl method that
|
||||
falls back to `run` plus a single final notification.
|
||||
|
||||
### Added
|
||||
|
||||
#### Observer + stop-conditions API
|
||||
|
||||
A new module `heuropt::observer` introduces:
|
||||
|
||||
- `Snapshot<'a, D>` — per-generation observation payload with
|
||||
`iteration`, `evaluations`, `elapsed`, `population`,
|
||||
`pareto_front`, `best`, and `objectives`.
|
||||
- `Observer<D>` trait — single method `observe(&Snapshot) ->
|
||||
ControlFlow<()>`. Closures of the right shape implement it
|
||||
automatically. `()` is the no-op observer.
|
||||
- `Optimizer::run_with(problem, observer)` — new method on the
|
||||
`Optimizer` trait with a default impl that falls back to `run`.
|
||||
Algorithms that override `run_with` (so far: `Nsga2`,
|
||||
`RandomSearch`, `DifferentialEvolution`) call the observer once
|
||||
per generation; others call it once at the end. Returning
|
||||
`ControlFlow::Break` halts the optimizer and returns the partial
|
||||
result.
|
||||
|
||||
#### Built-in observers (`observer::builtin`)
|
||||
|
||||
- `MaxTime(Duration)` — wall-clock cap.
|
||||
- `MaxIterations(usize)` — generation cap.
|
||||
- `TargetFitness(f64)` — direction-aware single-objective target.
|
||||
- `Stagnation { window, tolerance }` — halt when the best fitness
|
||||
hasn't improved by `tolerance` over `window` generations.
|
||||
- `Periodic::new(every, |snap| { … })` — call a user closure every
|
||||
`every` generations.
|
||||
- `AnyOf` / `AllOf` plus `Observer::or` / `Observer::and` for
|
||||
composition.
|
||||
- `TracingObserver` (behind the new `tracing` feature) — emits
|
||||
structured `debug!` events per generation.
|
||||
|
||||
#### Tracing feature
|
||||
|
||||
New optional feature `tracing`, gated on the
|
||||
[`tracing`](https://crates.io/crates/tracing) crate. Adds
|
||||
`TracingObserver` to the prelude when enabled.
|
||||
|
||||
#### Performance indicators
|
||||
|
||||
- `metrics::igd::igd` — Inverted Generational Distance against a
|
||||
reference set (typically the true Pareto front).
|
||||
- `metrics::igd::igd_plus` — Pareto-compliant IGD+ variant; adding
|
||||
a dominated point never improves the score.
|
||||
- `metrics::r2::r2` — R2 indicator using the weighted Tchebycheff
|
||||
utility. Pair with `pareto::das_dennis` for the canonical weight
|
||||
set.
|
||||
|
||||
#### Constrained example
|
||||
|
||||
`examples/constrained.rs` — solves the BNH constrained 2-objective
|
||||
problem (Binh & Korn 1996) with NSGA-II + the new observer API,
|
||||
demonstrating `Periodic` progress logging and `MaxTime` /
|
||||
composition.
|
||||
|
||||
### Changed
|
||||
|
||||
- `Population::as_slice()` — new convenience accessor.
|
||||
|
||||
[0.6.0]: https://github.com/swaits/heuropt/releases/tag/v0.6.0
|
||||
|
||||
## [0.5.0] — 2026-05-05
|
||||
|
||||
Theme: comprehensive documentation and project polish. No public-API
|
||||
changes — bumping `heuropt = "0.5"` in your `Cargo.toml` is enough.
|
||||
|
||||
### Added
|
||||
|
||||
#### User guide (mdbook)
|
||||
|
||||
A new mdbook user guide at `docs/book/`, deployed to
|
||||
<https://swaits.github.io/heuropt/> via a CI workflow on tag pushes.
|
||||
Chapters:
|
||||
|
||||
- **Introduction** — what heuropt is, who it's for, what's in the box.
|
||||
- **Five-minute walkthrough** — install, define a problem, run an
|
||||
optimizer, look at the result.
|
||||
- **Defining a problem** — the `Problem` trait in depth: single- vs
|
||||
multi-objective, constraints, custom decision types
|
||||
(`Vec<f64>`, `Vec<bool>`, `Vec<usize>`, custom structs).
|
||||
- **Choosing an algorithm** — the README's decision tree, expanded
|
||||
to a full chapter with the reasoning behind every branch.
|
||||
- **Cookbook** — seven recipes covering parallelism, expensive
|
||||
evaluations, comparison harnesses, permutation problems,
|
||||
constraint repair, picking one answer off a Pareto front, and
|
||||
writing your own optimizer.
|
||||
- **Comparison with other libraries** — heuropt vs pymoo, hyperopt,
|
||||
optuna, MOEA Framework, metaheuristics-rs, argmin. Honest about
|
||||
when *not* to pick heuropt.
|
||||
- **Stability and SemVer** — explicit guarantees about which surfaces
|
||||
are stable; what's likely to change before 1.0; bit-identical
|
||||
determinism contract.
|
||||
- **Migration guides** — per-release upgrade notes.
|
||||
|
||||
#### Runnable rustdoc examples
|
||||
|
||||
Every algorithm now has a runnable ` ```rust ` example block in its
|
||||
rustdoc — 35 algorithms, all exercised by `cargo test --doc`. Plus
|
||||
the existing crate-level example in `lib.rs` and the
|
||||
`CompositeVariation` operator example.
|
||||
|
||||
#### Real-world examples
|
||||
|
||||
Three new polished examples covering distinct domains:
|
||||
|
||||
- `examples/portfolio.rs` — multi-objective portfolio optimization
|
||||
with budget constraint via `ProjectToSimplex`. Pareto front of
|
||||
return-vs-risk trade-offs, plus a-posteriori weighted decision.
|
||||
- `examples/hyperparam_tuning.rs` — sample-efficient hyperparameter
|
||||
tuning with `BayesianOpt` and `Tpe`, demonstrating mixed-scale
|
||||
decoding (log-uniform learning rate, integer depth) and a 60-eval
|
||||
budget.
|
||||
- `examples/scheduling.rs` — single-machine weighted-completion-time
|
||||
scheduling: permutation decisions optimized via
|
||||
`SimulatedAnnealing` + `SwapMutation`, comparing against the
|
||||
Smith's-rule oracle.
|
||||
|
||||
#### Governance docs
|
||||
|
||||
- `CONTRIBUTING.md` — local-test checklist, conventional-commits
|
||||
requirement, contribution areas that land easily vs. those that
|
||||
need prior discussion.
|
||||
- `SECURITY.md` — disclosure policy, supported versions, what counts
|
||||
as a security issue.
|
||||
- `CODE_OF_CONDUCT.md` — adopts the
|
||||
[Builder's Code of Conduct](https://builderscode.org/) (CC0).
|
||||
- `.github/ISSUE_TEMPLATE/` — bug, feature, docs templates plus a
|
||||
`config.yml` that points security reports to the private
|
||||
vulnerability-disclosure flow.
|
||||
- `.github/PULL_REQUEST_TEMPLATE.md` — short, opinionated PR
|
||||
template.
|
||||
|
||||
#### CI / tooling
|
||||
|
||||
- `.github/workflows/docs.yml` — builds the mdbook user guide and
|
||||
deploys it to GitHub Pages on `main` pushes and tag pushes.
|
||||
|
||||
### Changed
|
||||
|
||||
- README hero block expanded with badges and a punchier opening;
|
||||
added explicit links to the user guide, the docs.rs API reference,
|
||||
and the testing-coverage breakdown.
|
||||
- `lib.rs` crate-level docs polished — better intro, points readers
|
||||
at the user guide and the design spec.
|
||||
|
||||
[0.5.0]: https://github.com/swaits/heuropt/releases/tag/v0.5.0
|
||||
|
||||
## [0.4.0] — 2026-05-05
|
||||
|
||||
Theme: testing infrastructure, two real bug fixes surfaced by that
|
||||
@@ -383,5 +542,5 @@ Initial release.
|
||||
`RandomSearch`, `Nsga2`, and `DifferentialEvolution`. Seeded runs stay
|
||||
bit-identical to serial mode.
|
||||
|
||||
[Unreleased]: https://github.com/swaits/heuropt/compare/v0.4.0...HEAD
|
||||
[Unreleased]: https://github.com/swaits/heuropt/compare/v0.6.0...HEAD
|
||||
[0.1.0]: https://github.com/swaits/heuropt/releases/tag/v0.1.0
|
||||
|
||||
@@ -0,0 +1,43 @@
|
||||
# Code of Conduct
|
||||
|
||||
heuropt adopts the [Builder's Code of Conduct](https://builderscode.org/),
|
||||
version 1.0.
|
||||
|
||||
A Code of Conduct for people who build things.
|
||||
|
||||
## The Rule
|
||||
|
||||
> "Stay professional. Stay technical."
|
||||
|
||||
## Expected
|
||||
|
||||
- Contribute constructively.
|
||||
- Respect others' time and work.
|
||||
- Focus on the work and its technical merit.
|
||||
|
||||
## Not Welcome
|
||||
|
||||
- Harassment, name-calling, or personal attacks.
|
||||
- Trolling, spamming, or derailing discussions.
|
||||
- Discussions about contributors rather than their contributions.
|
||||
|
||||
## Enforcement
|
||||
|
||||
Violations result in:
|
||||
|
||||
1. **Warning** — first offense.
|
||||
2. **Temporary suspension** — repeated or serious violations.
|
||||
3. **Permanent ban** — continued violations.
|
||||
|
||||
Maintainers can remove, block, or ban anyone who disrupts the project.
|
||||
|
||||
## Reporting
|
||||
|
||||
Email **steve@waits.net** with `[heuropt CoC]` in the subject line.
|
||||
Reports are handled confidentially.
|
||||
|
||||
---
|
||||
|
||||
The Builder's Code of Conduct is dedicated to the public domain under
|
||||
CC0 1.0 Universal. You may use, modify, and distribute it freely
|
||||
without attribution.
|
||||
+117
@@ -0,0 +1,117 @@
|
||||
# Contributing to heuropt
|
||||
|
||||
Thanks for considering a contribution. heuropt is a small, opinionated
|
||||
crate, but careful additions are welcome.
|
||||
|
||||
## Quick checklist
|
||||
|
||||
Before opening a pull request:
|
||||
|
||||
- [ ] `cargo fmt --all`
|
||||
- [ ] `cargo clippy --all-targets --all-features -- -D warnings`
|
||||
- [ ] `cargo test` (default features) and `cargo test --all-features`
|
||||
- [ ] `cargo doc --no-deps --all-features` with `RUSTDOCFLAGS="-D warnings"`
|
||||
- [ ] If you touched algorithm output: re-run `cargo run --release --example compare`
|
||||
and confirm the quality metrics did not change. Speed-only changes
|
||||
are required to be **bit-identical** against the prior snapshot.
|
||||
|
||||
CI runs all of the above on every PR; the matrix covers MSRV (1.85),
|
||||
the default / serde / parallel / serde+parallel feature combinations,
|
||||
and a 60-second fuzz soak per target.
|
||||
|
||||
## Commit style
|
||||
|
||||
Conventional Commits (https://www.conventionalcommits.org/) are
|
||||
required. The first line follows `<type>(<scope>): <summary>` where
|
||||
`<type>` is one of `feat`, `fix`, `perf`, `refactor`, `docs`, `test`,
|
||||
`chore`, `ci`, `build`, `style`. `<scope>` is the most specific module
|
||||
the change touches (e.g. `nsga2`, `hypervolume`, `pareto_archive`).
|
||||
|
||||
Bad: `Phase 1.1: Add core data types`
|
||||
Good: `feat(core): add data types and Rng alias`
|
||||
|
||||
Multiple logical changes in a single PR should be split into multiple
|
||||
commits, each on a single concern.
|
||||
|
||||
## What kinds of contributions land easily
|
||||
|
||||
- **Bug fixes** with a regression test that fails on `main` and passes
|
||||
on the fix.
|
||||
- **Performance wins** that preserve bit-identical output and include
|
||||
a `cargo bench` (gungraun) before/after, plus a `cargo run --release
|
||||
--example compare` diff confirming no quality regression.
|
||||
- **Documentation improvements** — missing rustdoc examples, README
|
||||
clarifications, mdbook chapters.
|
||||
- **New algorithms** that fit the established `Optimizer<P>` shape and
|
||||
ship with: a unit test, a property test (determinism + invariants),
|
||||
a comparison-harness entry, and rustdoc.
|
||||
- **New operators / metrics / Pareto utilities** with the same
|
||||
hygiene.
|
||||
|
||||
## What needs prior discussion
|
||||
|
||||
Open an issue before starting on:
|
||||
|
||||
- New traits or breaking changes to the public API surface.
|
||||
- A new optional feature flag.
|
||||
- Anything that depends on a heavy new dependency.
|
||||
- Restructuring of `src/algorithms/` or `src/pareto/`.
|
||||
|
||||
The crate intentionally keeps the trait surface small (`Problem`,
|
||||
`Optimizer`, `Initializer`, `Variation`, `Repair`); changes there
|
||||
are not refused but they need a clear motivation.
|
||||
|
||||
## Running the test suites locally
|
||||
|
||||
```sh
|
||||
# unit + integration + property tests
|
||||
cargo test
|
||||
|
||||
# all feature combinations
|
||||
cargo test --features serde
|
||||
cargo test --features parallel
|
||||
cargo test --all-features
|
||||
|
||||
# instruction-count benchmarks (needs valgrind installed)
|
||||
cargo bench
|
||||
|
||||
# coverage-guided fuzzing (needs nightly + cargo-fuzz)
|
||||
cd fuzz
|
||||
cargo +nightly fuzz run pareto_compare -- -max_total_time=60
|
||||
|
||||
# mutation testing (slow, optional)
|
||||
cargo install cargo-mutants
|
||||
cargo mutants
|
||||
```
|
||||
|
||||
## Reporting bugs
|
||||
|
||||
Please include:
|
||||
|
||||
1. The smallest reproducing input you can produce — ideally a 20-line
|
||||
`examples/repro.rs`.
|
||||
2. The exact command (`cargo run --release --example repro` etc.) and
|
||||
the observed vs expected output.
|
||||
3. The Rust toolchain (`rustc --version`) and feature flags.
|
||||
4. The heuropt version you saw the bug on.
|
||||
|
||||
Bugs that surface fuzz-target panics are particularly welcome; please
|
||||
attach the failing artifact (`fuzz/artifacts/<target>/crash-...`) so
|
||||
we can add it to the regression-test corpus.
|
||||
|
||||
## Security
|
||||
|
||||
For security concerns please follow the disclosure policy in
|
||||
[SECURITY.md](SECURITY.md). Don't open public issues for security
|
||||
bugs.
|
||||
|
||||
## Code of conduct
|
||||
|
||||
This project follows the [Builder's Code of Conduct](CODE_OF_CONDUCT.md).
|
||||
The short version: stay professional, stay technical, focus on the
|
||||
work and its merit.
|
||||
|
||||
## License
|
||||
|
||||
By submitting a contribution, you agree that your work is licensed
|
||||
under the same MIT license as the rest of heuropt.
|
||||
+3
-1
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "heuropt"
|
||||
version = "0.4.0"
|
||||
version = "0.6.0"
|
||||
edition = "2024"
|
||||
rust-version = "1.85"
|
||||
authors = ["Stephen Waits <steve@waits.net>"]
|
||||
@@ -17,12 +17,14 @@ categories = ["algorithms", "science", "mathematics", "simulation"]
|
||||
default = []
|
||||
serde = ["dep:serde"]
|
||||
parallel = ["dep:rayon"]
|
||||
tracing = ["dep:tracing"]
|
||||
|
||||
[dependencies]
|
||||
rand = "0.9"
|
||||
rand_distr = "0.5"
|
||||
rayon = { version = "1", optional = true }
|
||||
serde = { version = "1", features = ["derive"], optional = true }
|
||||
tracing = { version = "0.1", optional = true, default-features = false, features = ["std", "attributes"] }
|
||||
|
||||
[dev-dependencies]
|
||||
gungraun = "0.18"
|
||||
|
||||
@@ -2,28 +2,39 @@
|
||||
|
||||
[](https://crates.io/crates/heuropt)
|
||||
[](https://docs.rs/heuropt)
|
||||
[](https://swaits.github.io/heuropt/)
|
||||
[](LICENSE)
|
||||
[](https://github.com/swaits/heuropt/actions/workflows/ci.yml)
|
||||
|
||||
A practical Rust toolkit for implementing heuristic single-objective,
|
||||
multi-objective, and many-objective optimization algorithms.
|
||||
**A practical Rust toolkit for heuristic optimization.** Single-objective.
|
||||
Multi-objective. Many-objective. 35 algorithms. One small set of traits.
|
||||
Bit-identical seeded determinism. No trait objects, no GATs, no generic-RNG
|
||||
plumbing in the public API.
|
||||
|
||||
`heuropt` is **not** a research framework full of abstract machinery — it is a
|
||||
small set of concrete types, a handful of simple traits, and a few reference
|
||||
algorithms. The goal: an entry-level Rust engineer can define a problem, run a
|
||||
built-in optimizer, or implement a new optimizer without learning any
|
||||
framework concepts.
|
||||
If you can write a `Problem` impl and read `RandomSearch`, you can write your
|
||||
own optimizer. That's the whole pitch.
|
||||
|
||||
- 📖 **Read the [user guide](https://swaits.github.io/heuropt/)** for tutorials,
|
||||
cookbook recipes, comparison with pymoo / hyperopt / MOEA Framework, and
|
||||
stability policy.
|
||||
- 🔧 **[API reference on docs.rs](https://docs.rs/heuropt)** has runnable
|
||||
` ```rust ` examples on every algorithm.
|
||||
- 🧪 Tested with **316+ unit / integration / property tests** plus 8
|
||||
cargo-fuzz targets running on every PR.
|
||||
- ⚡ Hot paths heavily optimized — comparison harness 3.27× faster as of
|
||||
v0.4.0, all bit-identical to the reference output.
|
||||
|
||||
## Installation
|
||||
|
||||
```toml
|
||||
[dependencies]
|
||||
heuropt = "0.3"
|
||||
heuropt = "0.5"
|
||||
|
||||
# Optional features:
|
||||
# - "serde": derive Serialize/Deserialize on the core data types.
|
||||
# - "parallel": evaluate populations across rayon's thread pool.
|
||||
# Seeded runs stay bit-identical to serial mode.
|
||||
# heuropt = { version = "0.3", features = ["serde", "parallel"] }
|
||||
# heuropt = { version = "0.5", features = ["serde", "parallel"] }
|
||||
```
|
||||
|
||||
## Define a problem
|
||||
@@ -504,6 +515,16 @@ heuropt is exhaustively tested across several layers:
|
||||
- **CI** (`.github/workflows/ci.yml`) — fmt, clippy
|
||||
(`-D warnings`), test (4-feature matrix), doc, MSRV (1.85), fuzz.
|
||||
|
||||
## Contributing
|
||||
|
||||
See [CONTRIBUTING.md](CONTRIBUTING.md) for the local-test checklist,
|
||||
conventional-commits requirement, and project-governance docs.
|
||||
|
||||
This project follows the [Builder's Code of Conduct](CODE_OF_CONDUCT.md):
|
||||
stay professional, stay technical, focus on the work and its merit.
|
||||
|
||||
For security disclosures, see [SECURITY.md](SECURITY.md).
|
||||
|
||||
## License
|
||||
|
||||
MIT — see [LICENSE](LICENSE).
|
||||
|
||||
+62
@@ -0,0 +1,62 @@
|
||||
# Security policy
|
||||
|
||||
## Supported versions
|
||||
|
||||
Security fixes are applied to the latest released minor version on
|
||||
crates.io. Patch-level releases (`0.x.y` → `0.x.y+1`) are issued as
|
||||
needed.
|
||||
|
||||
| Version | Supported |
|
||||
|---------|--------------------|
|
||||
| 0.5.x | ✅ |
|
||||
| ≤ 0.4.x | ❌ (please upgrade) |
|
||||
|
||||
heuropt is pre-1.0; the public API may change between minor versions.
|
||||
Once 1.0.0 ships, the support window will be at least the latest two
|
||||
minor versions.
|
||||
|
||||
## Reporting a vulnerability
|
||||
|
||||
Please **do not** open a public GitHub issue for a security bug.
|
||||
Instead use one of these channels:
|
||||
|
||||
- GitHub's [private vulnerability reporting](https://github.com/swaits/heuropt/security/advisories/new)
|
||||
on the repository.
|
||||
- Email **steve@waits.net** with subject line `[heuropt security]
|
||||
<short summary>`.
|
||||
|
||||
Please include:
|
||||
|
||||
1. A description of the vulnerability and the affected versions.
|
||||
2. The smallest reproducer you can produce — a `cargo run --example
|
||||
repro` is ideal.
|
||||
3. Your assessment of impact and exploitability.
|
||||
4. Any suggested mitigation if you have one.
|
||||
|
||||
## What I will do
|
||||
|
||||
- Acknowledge the report within **72 hours**.
|
||||
- Confirm or refute reproducibility within **7 days**.
|
||||
- Issue a fix in a patch release within **30 days** for confirmed
|
||||
high-severity issues; less urgent issues may roll into the next
|
||||
minor release.
|
||||
- Credit the reporter in the CHANGELOG entry unless you ask
|
||||
otherwise.
|
||||
|
||||
## What counts as a security issue
|
||||
|
||||
heuropt is a numerical library, not a network service or sandbox. The
|
||||
realistic security-relevant categories are:
|
||||
|
||||
- **Memory safety**: any unsafe-code-related UB or unwinds-across-FFI
|
||||
bug. heuropt itself uses no `unsafe`; this category covers
|
||||
dependencies it transitively pulls in.
|
||||
- **Denial of service**: an input to a public API that causes
|
||||
unbounded memory growth, infinite loop, or panic outside its
|
||||
documented panic conditions. (Documented panics for invalid config
|
||||
are not bugs.)
|
||||
- **Supply-chain compromise**: a published heuropt crate that doesn't
|
||||
match the source on the tagged commit.
|
||||
|
||||
Functional correctness bugs (an algorithm produces wrong
|
||||
hypervolumes, etc.) are tracked as ordinary issues, not security.
|
||||
@@ -0,0 +1,34 @@
|
||||
[book]
|
||||
title = "heuropt — the user guide"
|
||||
description = "A practical Rust toolkit for heuristic single-, multi-, and many-objective optimization."
|
||||
authors = ["Stephen Waits"]
|
||||
language = "en"
|
||||
src = "src"
|
||||
|
||||
[build]
|
||||
build-dir = "../../target/book"
|
||||
create-missing = false
|
||||
|
||||
[output.html]
|
||||
default-theme = "rust"
|
||||
preferred-dark-theme = "navy"
|
||||
git-repository-url = "https://github.com/swaits/heuropt"
|
||||
edit-url-template = "https://github.com/swaits/heuropt/edit/main/docs/book/{path}"
|
||||
site-url = "/heuropt/"
|
||||
no-section-label = true
|
||||
|
||||
[output.html.fold]
|
||||
enable = true
|
||||
level = 1
|
||||
|
||||
[output.html.search]
|
||||
enable = true
|
||||
limit-results = 30
|
||||
teaser-word-count = 30
|
||||
use-boolean-and = true
|
||||
|
||||
[output.html.print]
|
||||
enable = true
|
||||
|
||||
[rust]
|
||||
edition = "2024"
|
||||
@@ -0,0 +1,26 @@
|
||||
# Summary
|
||||
|
||||
[Introduction](./introduction.md)
|
||||
|
||||
# Getting started
|
||||
|
||||
- [Five-minute walkthrough](./getting-started.md)
|
||||
- [Defining a problem](./defining-problems.md)
|
||||
- [Choosing an algorithm](./choosing-an-algorithm.md)
|
||||
|
||||
# Cookbook
|
||||
|
||||
- [Recipes](./cookbook.md)
|
||||
- [Parallelize evaluation with rayon](./cookbook/parallel.md)
|
||||
- [Tune a model with expensive evaluations](./cookbook/expensive-evaluations.md)
|
||||
- [Compare two algorithms on your problem](./cookbook/compare.md)
|
||||
- [Optimize a permutation (TSP-style)](./cookbook/permutation.md)
|
||||
- [Constrain your search with `Repair`](./cookbook/constraints.md)
|
||||
- [Pick one answer off a Pareto front](./cookbook/pick-one.md)
|
||||
- [Write your own algorithm](./cookbook/custom-optimizer.md)
|
||||
|
||||
# Reference
|
||||
|
||||
- [Comparison with other libraries](./comparison.md)
|
||||
- [Stability and SemVer](./stability.md)
|
||||
- [Migration guides](./migration.md)
|
||||
@@ -0,0 +1,285 @@
|
||||
# Choosing an algorithm
|
||||
|
||||
The README has a compact decision tree. This chapter expands it with
|
||||
the *reasoning* behind each branch.
|
||||
|
||||
## Step 0: How expensive is one evaluation?
|
||||
|
||||
This is the first fork because it changes everything that comes
|
||||
after it.
|
||||
|
||||
| Eval cost | Budget you can afford | Algorithm family |
|
||||
|----------------------------|---------------------------|-----------------------------|
|
||||
| Microseconds (pure math) | 10 000 – 1 000 000 evals | Population-based |
|
||||
| Milliseconds (sim, IO) | 1 000 – 10 000 evals | Population-based |
|
||||
| Seconds (small training) | 100 – 1 000 evals | Sample-efficient (BO, TPE) |
|
||||
| Minutes+ (full training) | 50 – 500 evals | Sample-efficient + multi-fidelity |
|
||||
|
||||
For the cheap-eval branch, you have the run of the catalog. For the
|
||||
expensive branch, classical evolutionary methods waste your evaluation
|
||||
budget — go to [`BayesianOpt`] or [`Tpe`]. For the *very* expensive
|
||||
branch where each eval has a tunable budget (epochs, MC samples, sim
|
||||
steps), [`Hyperband`] over the [`PartialProblem`] trait is the move.
|
||||
|
||||
## Step 1: How many objectives?
|
||||
|
||||
The biggest fork.
|
||||
|
||||
- **One** — there's a single best answer. Pick from the
|
||||
single-objective branch.
|
||||
- **Two or three** — a Pareto front. Pick from the multi-objective
|
||||
branch.
|
||||
- **Four or more** — a many-objective Pareto front; classical
|
||||
multi-objective methods break down here because almost every pair
|
||||
of points is non-dominated. Pick from the many-objective branch.
|
||||
|
||||
> **Pareto front:** the set of decisions where you cannot improve any
|
||||
> objective without sacrificing another. In a 2-objective minimize
|
||||
> problem, plot every solution; the Pareto front is the lower-left
|
||||
> envelope.
|
||||
|
||||
If you found yourself staring at a single composite score that's a
|
||||
weighted sum of conflicting goals, you probably have a multi-objective
|
||||
problem in disguise. A weighted sum bakes in your preferences before
|
||||
you've seen the trade-off; running a multi-objective optimizer first
|
||||
and picking off the front later is almost always a better workflow
|
||||
(see [Pick one answer off a Pareto front](./cookbook/pick-one.md)).
|
||||
|
||||
## Step 2 — single-objective continuous
|
||||
|
||||
These all take `Vec<f64>` decisions.
|
||||
|
||||
### Smooth, low-to-moderate dimension
|
||||
|
||||
[`CmaEs`] is the strong default. It adapts the search distribution's
|
||||
covariance to the local landscape. On the comparison harness it
|
||||
hits machine epsilon on Rosenbrock at 30 000 evaluations.
|
||||
|
||||
For very low-dimensional smooth problems (≤ 5 dim), [`NelderMead`] is
|
||||
deterministic and converges to f = 0 exactly on Rosenbrock.
|
||||
|
||||
### High dimension, smooth
|
||||
|
||||
[`SeparableNes`] uses a diagonal covariance — cheaper per step than
|
||||
CmaEs at the cost of being unable to model rotated landscapes. Worth
|
||||
trying when CmaEs's `O(d²)` per-step cost hurts.
|
||||
|
||||
### Multimodal landscapes
|
||||
|
||||
Multimodal = many local minima that aren't the global one. Rastrigin
|
||||
and Ackley are classic traps.
|
||||
|
||||
[`IpopCmaEs`] is CmaEs with an increasing-population restart strategy
|
||||
specifically designed for this. On the harness it drops vanilla CmaEs's
|
||||
Rastrigin score from f = 2.35 to f = 0.13.
|
||||
|
||||
[`DifferentialEvolution`] is rarely beaten on cheap multimodal
|
||||
continuous problems. On Rastrigin it ties with `(1+1)-ES` at f = 0.
|
||||
|
||||
[`SimulatedAnnealing`] is a cheap, generic baseline that escapes local
|
||||
optima via temperature decay.
|
||||
|
||||
### Want parameter-free
|
||||
|
||||
[`Tlbo`] (Teaching-Learning-Based Optimization) has no `F`, `CR`, `w`,
|
||||
or `σ` to tune. Often a respectable middle-of-the-pack performer.
|
||||
|
||||
### Smallest possible self-adapting baseline
|
||||
|
||||
[`OnePlusOneEs`] — Rechenberg's 1973 `(1+1)`-ES with the one-fifth
|
||||
success rule. On the harness it hits f = 0 on Rastrigin in 50 000
|
||||
evaluations.
|
||||
|
||||
### Just want a baseline
|
||||
|
||||
[`RandomSearch`]. Useful as a sanity check: if your fancy optimizer
|
||||
can't beat random search, something is wrong (with the fancy
|
||||
optimizer or with the problem).
|
||||
|
||||
## Step 2 — single-objective other types
|
||||
|
||||
| Decision type | Algorithm | Notes |
|
||||
|---|---|---|
|
||||
| `Vec<bool>` | [`Umda`] | Per-bit marginal EDA. Independent-bit assumption. |
|
||||
| `Vec<bool>` | [`GeneticAlgorithm`] + [`BitFlipMutation`] | When bit interactions matter. |
|
||||
| `Vec<usize>` (permutation) | [`AntColonyTsp`] | TSP-style with a distance matrix. |
|
||||
| `Vec<usize>` (permutation) | [`SimulatedAnnealing`] + [`SwapMutation`] | Generic discrete baseline. |
|
||||
| `Vec<usize>` or custom | [`TabuSearch`] | You supply the neighbor function. |
|
||||
| Custom struct | [`SimulatedAnnealing`] / [`HillClimber`] | With your own `Variation` impl. |
|
||||
|
||||
## Step 2 — multi-objective (2 or 3)
|
||||
|
||||
### Strong default
|
||||
|
||||
[`Nsga2`] is the canonical Pareto-based EA. Fast, well-understood,
|
||||
maintains diversity via crowding distance. On the harness it lands
|
||||
on the Pareto front of every test problem.
|
||||
|
||||
### Real-valued, smooth front, want best convergence
|
||||
|
||||
[`Mopso`] (multi-objective PSO with archive). On ZDT1 it wins
|
||||
hypervolume outright and converges 100× tighter than the
|
||||
dominance-based methods.
|
||||
|
||||
### Better front quality than NSGA-II
|
||||
|
||||
[`Ibea`] (indicator-based) is consistently the best of the
|
||||
dominance-based methods on the harness — wins ZDT3 hypervolume and
|
||||
DTLZ2 mean distance by 24×. It uses an additive ε-indicator for
|
||||
selection rather than dominance + crowding.
|
||||
|
||||
[`Spea2`] (strength + density) — solid alternative; explicit external
|
||||
archive separate from the population.
|
||||
|
||||
[`SmsEmoa`] uses exact hypervolume contribution for selection. Elegant
|
||||
in theory; in practice on the harness budgets here it underperforms
|
||||
NSGA-II. Worth the higher per-step cost only when exact HV
|
||||
contribution is the right discriminator.
|
||||
|
||||
### Decomposition / weight-vector style
|
||||
|
||||
[`Moead`] decomposes the multi-objective problem into many scalar
|
||||
sub-problems (Tchebycheff or weighted sum) and solves them in
|
||||
parallel. Very fast per generation; scales naturally to many
|
||||
objectives.
|
||||
|
||||
### Disconnected or non-convex front
|
||||
|
||||
[`AgeMoea`] estimates the front geometry adaptively (the L_p
|
||||
parameter `p` is fit from data each generation).
|
||||
|
||||
[`Knea`] favors knee points — the regions of the front where small
|
||||
gains in one objective cost large losses in another.
|
||||
|
||||
[`Ibea`] also handles disconnected fronts well.
|
||||
|
||||
### Region-based diversity
|
||||
|
||||
[`PesaII`] uses grid hyperboxes to drive selection — divide the
|
||||
objective space into a grid, pick from the least-crowded boxes.
|
||||
|
||||
[`EpsilonMoea`] uses an ε-grid archive that auto-limits its size.
|
||||
|
||||
### Just one starting decision (no population budget)
|
||||
|
||||
[`Paes`] — `(1+1)`-ES with a Pareto archive. Cheap, simple, useful
|
||||
when your evaluations are expensive enough that you can't afford a
|
||||
population.
|
||||
|
||||
## Step 2 — many-objective (4+)
|
||||
|
||||
### Linear / simplex-shaped front (e.g., DTLZ1)
|
||||
|
||||
[`Grea`] — grid coords drive ranking. On DTLZ1 it beats NSGA-III by
|
||||
3× and AGE-MOEA by 2.5×.
|
||||
|
||||
[`Moead`] — decomposition shines on linear fronts; second on DTLZ1
|
||||
and among the fastest per generation.
|
||||
|
||||
### Curved / unknown front geometry
|
||||
|
||||
[`Nsga3`] — reference-point niching; canonical many-objective method;
|
||||
strong default when the front isn't simplex-shaped.
|
||||
|
||||
[`AgeMoea`] — estimates L_p geometry per generation.
|
||||
|
||||
[`Rvea`] — reference vectors with adaptive penalty.
|
||||
|
||||
### Indicator-based selection
|
||||
|
||||
[`Ibea`] — additive ε-indicator; doesn't degrade at high obj count.
|
||||
|
||||
[`HypE`] — Monte Carlo hypervolume estimation; scales to arbitrary
|
||||
objective count where exact HV is too expensive.
|
||||
|
||||
## Step 3: Are there hard constraints?
|
||||
|
||||
heuropt models constraints as a single scalar `constraint_violation`
|
||||
on each `Evaluation`. Three escalations when the feasibility region
|
||||
is hard to find:
|
||||
|
||||
1. **Penalty-only.** Just set `constraint_violation > 0` for
|
||||
infeasible decisions. The default tournament/Pareto comparisons
|
||||
prefer feasibles automatically.
|
||||
2. **Repair.** Implement [`Repair<D>`] (or use the provided
|
||||
[`ClampToBounds`] / [`ProjectToSimplex`]) to project infeasible
|
||||
decisions back into the feasible region. Pair with a `Variation`
|
||||
in a [`CompositeVariation`] for bounds-aware variants.
|
||||
3. **Stochastic ranking.** Use [`stochastic_ranking_select`] instead
|
||||
of `tournament_select_single_objective`. It probabilistically
|
||||
explores near-feasibility instead of strict feasibility-first
|
||||
ordering, which helps when feasible regions are narrow.
|
||||
|
||||
See [Constrain your search with `Repair`](./cookbook/constraints.md)
|
||||
for worked examples.
|
||||
|
||||
## Step 4: Should you parallelize?
|
||||
|
||||
Enable the `parallel` feature flag if your `evaluate` takes more
|
||||
than ~50 µs. Population-based algorithms ([`RandomSearch`], [`Nsga2`],
|
||||
[`DifferentialEvolution`], [`Spea2`], [`Ibea`], [`Mopso`], …) batch-
|
||||
evaluate via rayon when the feature is on. **Seeded runs stay
|
||||
bit-identical** to serial mode.
|
||||
|
||||
```toml
|
||||
heuropt = { version = "0.5", features = ["parallel"] }
|
||||
```
|
||||
|
||||
## TL;DR table
|
||||
|
||||
| Situation | Pick |
|
||||
|---|---|
|
||||
| Smooth single-objective continuous | [`CmaEs`] |
|
||||
| Multimodal single-objective continuous | [`IpopCmaEs`] or [`DifferentialEvolution`] |
|
||||
| Expensive single-objective | [`BayesianOpt`] or [`Tpe`] |
|
||||
| Multi-fidelity single-objective | [`Hyperband`] |
|
||||
| 2- or 3-objective default | [`Nsga2`] |
|
||||
| 2-objective real-valued smooth front | [`Mopso`] |
|
||||
| Disconnected / non-convex front | [`Ibea`] |
|
||||
| Many-objective default (curved front) | [`Nsga3`] |
|
||||
| Many-objective linear / simplex front | [`Grea`] |
|
||||
| Permutation problem | [`AntColonyTsp`] |
|
||||
| Binary problem | [`Umda`] |
|
||||
| Custom decision type | [`SimulatedAnnealing`] + your `Variation` |
|
||||
| Sanity baseline | [`RandomSearch`] |
|
||||
|
||||
[`CmaEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/cma_es/struct.CmaEs.html
|
||||
[`IpopCmaEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ipop_cma_es/struct.IpopCmaEs.html
|
||||
[`SeparableNes`]: https://docs.rs/heuropt/latest/heuropt/algorithms/snes/struct.SeparableNes.html
|
||||
[`NelderMead`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nelder_mead/struct.NelderMead.html
|
||||
[`DifferentialEvolution`]: https://docs.rs/heuropt/latest/heuropt/algorithms/differential_evolution/struct.DifferentialEvolution.html
|
||||
[`SimulatedAnnealing`]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
|
||||
[`Tlbo`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tlbo/struct.Tlbo.html
|
||||
[`OnePlusOneEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/one_plus_one_es/struct.OnePlusOneEs.html
|
||||
[`RandomSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/random_search/struct.RandomSearch.html
|
||||
[`HillClimber`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hill_climber/struct.HillClimber.html
|
||||
[`BayesianOpt`]: https://docs.rs/heuropt/latest/heuropt/algorithms/bayesian_opt/struct.BayesianOpt.html
|
||||
[`Tpe`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tpe/struct.Tpe.html
|
||||
[`Hyperband`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hyperband/struct.Hyperband.html
|
||||
[`PartialProblem`]: https://docs.rs/heuropt/latest/heuropt/core/partial_problem/trait.PartialProblem.html
|
||||
[`Umda`]: https://docs.rs/heuropt/latest/heuropt/algorithms/umda/struct.Umda.html
|
||||
[`GeneticAlgorithm`]: https://docs.rs/heuropt/latest/heuropt/algorithms/genetic_algorithm/struct.GeneticAlgorithm.html
|
||||
[`BitFlipMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.BitFlipMutation.html
|
||||
[`AntColonyTsp`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ant_colony_tsp/struct.AntColonyTsp.html
|
||||
[`SwapMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.SwapMutation.html
|
||||
[`TabuSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tabu_search/struct.TabuSearch.html
|
||||
[`Nsga2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga2/struct.Nsga2.html
|
||||
[`Nsga3`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga3/struct.Nsga3.html
|
||||
[`Mopso`]: https://docs.rs/heuropt/latest/heuropt/algorithms/mopso/struct.Mopso.html
|
||||
[`Ibea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ibea/struct.Ibea.html
|
||||
[`Spea2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/spea2/struct.Spea2.html
|
||||
[`SmsEmoa`]: https://docs.rs/heuropt/latest/heuropt/algorithms/sms_emoa/struct.SmsEmoa.html
|
||||
[`Moead`]: https://docs.rs/heuropt/latest/heuropt/algorithms/moead/struct.Moead.html
|
||||
[`AgeMoea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/age_moea/struct.AgeMoea.html
|
||||
[`Knea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/knea/struct.Knea.html
|
||||
[`PesaII`]: https://docs.rs/heuropt/latest/heuropt/algorithms/pesa2/struct.PesaII.html
|
||||
[`EpsilonMoea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/epsilon_moea/struct.EpsilonMoea.html
|
||||
[`Paes`]: https://docs.rs/heuropt/latest/heuropt/algorithms/paes/struct.Paes.html
|
||||
[`Grea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/grea/struct.Grea.html
|
||||
[`Rvea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/rvea/struct.Rvea.html
|
||||
[`HypE`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hype/struct.Hype.html
|
||||
[`Repair<D>`]: https://docs.rs/heuropt/latest/heuropt/traits/trait.Repair.html
|
||||
[`ClampToBounds`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.ClampToBounds.html
|
||||
[`ProjectToSimplex`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.ProjectToSimplex.html
|
||||
[`stochastic_ranking_select`]: https://docs.rs/heuropt/latest/heuropt/selection/tournament/fn.stochastic_ranking_select.html
|
||||
[`CompositeVariation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.CompositeVariation.html
|
||||
@@ -0,0 +1,108 @@
|
||||
# Comparison with other libraries
|
||||
|
||||
heuropt is one of many heuristic-optimization libraries. This chapter
|
||||
is an honest, opinionated comparison to help you choose.
|
||||
|
||||
The columns:
|
||||
|
||||
- **Lang** — primary implementation language.
|
||||
- **Algorithms** — rough catalog count.
|
||||
- **Multi-obj** — built-in support for Pareto-based multi-objective
|
||||
optimization.
|
||||
- **Surrogates** — built-in Bayesian / TPE / multi-fidelity.
|
||||
- **Determinism** — seeded reproducibility as a first-class property.
|
||||
- **Async / async-eval** — first-class async runtime support.
|
||||
|
||||
| Library | Lang | Algorithms | Multi-obj | Surrogates | Determinism | Async |
|
||||
|---|---|---|---|---|---|---|
|
||||
| **heuropt 0.5** | Rust | 35 | ✅ NSGA-II/III, SPEA2, IBEA, MOEA/D, MOPSO, SMS-EMOA, HypE, AGE-MOEA, GrEA, KnEA, RVEA, PESA-II, ε-MOEA, PAES | ✅ BO, TPE, Hyperband | ✅ bit-identical seeded | ⏳ planned |
|
||||
| pymoo | Python | ~25 | ✅ extensive | partial (BO via plug-ins) | ✅ | ❌ |
|
||||
| DEAP | Python | flexible toolbox | ✅ | ❌ | ✅ | ❌ |
|
||||
| hyperopt | Python | TPE-focused | ❌ | ✅ TPE | partial | partial |
|
||||
| optuna | Python | TPE / CMA-ES / NSGA-II | ✅ | ✅ TPE, BoTorch via plug-in | ✅ | ✅ |
|
||||
| MOEA Framework | Java | ~40 | ✅ very extensive | ❌ | ✅ | ❌ |
|
||||
| metaheuristics-rs | Rust | ~10 | partial | ❌ | ✅ | ❌ |
|
||||
| argmin | Rust | line-search / quasi-Newton | ❌ | ❌ | ✅ | ❌ |
|
||||
|
||||
## When to pick heuropt
|
||||
|
||||
- You're working in **Rust** and want a single, dependency-light crate
|
||||
for evolutionary / metaheuristic optimization.
|
||||
- You need **multi-objective or many-objective** algorithms (12+
|
||||
Pareto-aware methods in the catalog) AND you don't want to glue
|
||||
Python into your Rust pipeline.
|
||||
- You want **bit-identical determinism**: same seed produces same
|
||||
output, on every machine, across releases unless explicitly noted
|
||||
otherwise.
|
||||
- You want a **small, readable codebase** — every algorithm is
|
||||
written for clarity, no trait-object plumbing, no GATs in user-
|
||||
facing APIs. Reading `RandomSearch` should be enough to write a
|
||||
new optimizer.
|
||||
|
||||
## When *not* to pick heuropt
|
||||
|
||||
- You need **first-class async / await** for evaluations that talk to
|
||||
HTTP services or spawn subprocesses. heuropt is sync; that's on
|
||||
the roadmap but not shipping yet.
|
||||
- You need **gradient-based** optimization. Use `argmin` (Rust) or
|
||||
`scipy.optimize` (Python) — heuropt is gradient-free by design.
|
||||
- You need **GPU-accelerated** evaluations. heuropt's `evaluate`
|
||||
function runs on CPU; use Python (jax/torch) or roll your own
|
||||
GPU pipeline.
|
||||
- You need **distributed multi-machine** evaluation. heuropt
|
||||
parallelizes within one process via rayon. Distribution is up to
|
||||
you (split the seeds across machines, aggregate).
|
||||
- You're comfortable in Python and pymoo / optuna already cover
|
||||
your problem. heuropt's value-add over pymoo is mostly that it's
|
||||
Rust — if that doesn't matter to you, the Python ecosystem has more
|
||||
battle-tested integrations.
|
||||
|
||||
## Algorithm coverage at a glance
|
||||
|
||||
heuropt covers the same major Pareto MOEAs as pymoo and MOEA Framework:
|
||||
NSGA-II/III, SPEA2, IBEA, MOEA/D, MOPSO, SMS-EMOA, HypE, AGE-MOEA,
|
||||
GrEA, KnEA, RVEA, PESA-II, ε-MOEA, PAES.
|
||||
|
||||
The expensive-evaluation regime: BayesianOpt + TPE + Hyperband. This
|
||||
is comparable to optuna's coverage but in pure Rust.
|
||||
|
||||
The single-objective continuous catalog (CMA-ES, IPOP-CMA-ES, sNES,
|
||||
DE, PSO, GA, TLBO, (1+1)-ES, NelderMead, RandomSearch, HillClimber,
|
||||
SimulatedAnnealing) covers the canonical baselines and several modern
|
||||
variants.
|
||||
|
||||
What heuropt does **not** ship that some libraries do:
|
||||
|
||||
- **Re-themed metaphor metaheuristics** (Whale Optimization, Grey
|
||||
Wolf, Bat, Firefly, Harris Hawks, etc.). These are cut from the
|
||||
catalog deliberately — they are mostly DE/PSO with new names. If
|
||||
you specifically need one, please open an issue with citations.
|
||||
- **Non-evolutionary global optimizers** like dual annealing or
|
||||
basin-hopping (use `scipy.optimize` for those).
|
||||
- **A web UI / dashboard** like optuna's. heuropt is library-only.
|
||||
|
||||
## Speed
|
||||
|
||||
heuropt's hot paths (Pareto utilities, hypervolume, key inner loops)
|
||||
are heavily optimized — see the perf entry in the v0.4.0 CHANGELOG.
|
||||
On the comparison harness in `examples/compare.rs` (10-seed mean,
|
||||
30 000 evaluations on DTLZ2), the total wall-clock time across 12
|
||||
algorithms is ~5 seconds. Per-algorithm timings are in
|
||||
[`examples/compare-results.md`](https://github.com/swaits/heuropt/blob/main/examples/compare-results.md).
|
||||
|
||||
For comparison-shopping speed against Python libraries, the gap is
|
||||
typically 10×–100× in heuropt's favor for compute-bound
|
||||
`evaluate` functions, because Rust skips the Python-loop overhead. If
|
||||
your `evaluate` calls into NumPy/PyTorch and those are the bottleneck,
|
||||
the gap shrinks substantially.
|
||||
|
||||
## Honest weakness: ecosystem
|
||||
|
||||
The biggest thing pymoo / optuna / DEAP have that heuropt doesn't:
|
||||
**community + plug-ins + tutorials**. They've been around longer and
|
||||
have rich third-party integrations (visualization, MLflow,
|
||||
Hyperband+BO hybrids, distributed runners). heuropt is younger; the
|
||||
core is solid but the ecosystem is small.
|
||||
|
||||
If you adopt heuropt and miss a thing, the project is small enough
|
||||
that contributions land fast. See [CONTRIBUTING.md](https://github.com/swaits/heuropt/blob/main/CONTRIBUTING.md).
|
||||
@@ -0,0 +1,26 @@
|
||||
# Cookbook
|
||||
|
||||
Short, focused recipes for the patterns that come up in practice.
|
||||
Each recipe is self-contained and small enough to copy into your own
|
||||
project.
|
||||
|
||||
## Recipes
|
||||
|
||||
- [Parallelize evaluation with rayon](./cookbook/parallel.md) — when
|
||||
your `evaluate` is non-trivial, the `parallel` feature pays for
|
||||
itself almost immediately.
|
||||
- [Tune a model with expensive evaluations](./cookbook/expensive-evaluations.md)
|
||||
— `BayesianOpt`, `Tpe`, and `Hyperband` for the 50–500-eval
|
||||
regime.
|
||||
- [Compare two algorithms on your problem](./cookbook/compare.md) —
|
||||
multi-seed harness pattern straight from `examples/compare.rs`.
|
||||
- [Optimize a permutation (TSP-style)](./cookbook/permutation.md) —
|
||||
`AntColonyTsp` with a distance matrix.
|
||||
- [Constrain your search with `Repair`](./cookbook/constraints.md) —
|
||||
bounds, simplex projection, custom repair.
|
||||
- [Pick one answer off a Pareto front](./cookbook/pick-one.md) — the
|
||||
a-posteriori weighted-decision pattern from the `jiggly_tuning`
|
||||
example.
|
||||
- [Write your own algorithm](./cookbook/custom-optimizer.md) —
|
||||
implement `Optimizer<P>` from scratch, à la the
|
||||
`examples/custom_optimizer.rs` walkthrough.
|
||||
@@ -0,0 +1,148 @@
|
||||
# Compare two algorithms on your problem
|
||||
|
||||
The harness in `examples/compare.rs` runs every applicable algorithm
|
||||
against every test problem with N seeds and reports mean ± std.
|
||||
You can lift the same pattern for your own problem in ~30 lines.
|
||||
|
||||
## The pattern
|
||||
|
||||
1. Wrap your problem in a struct that implements [`Problem`].
|
||||
2. Pick a few candidate algorithms.
|
||||
3. For each algorithm × seed, run and record the metric you care about.
|
||||
4. Print mean ± std.
|
||||
|
||||
## Worked example
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
use std::time::Instant;
|
||||
|
||||
struct MyProblem;
|
||||
impl Problem for MyProblem {
|
||||
type Decision = Vec<f64>;
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
}
|
||||
fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
// your problem here
|
||||
Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
}
|
||||
}
|
||||
|
||||
const SEEDS: u64 = 10;
|
||||
const DIM: usize = 5;
|
||||
const BUDGET: usize = 30_000;
|
||||
|
||||
fn main() {
|
||||
let bounds: Vec<(f64, f64)> = vec![(-5.0, 5.0); DIM];
|
||||
|
||||
let mut best_de = vec![];
|
||||
let mut best_cmaes = vec![];
|
||||
let mut best_ipop = vec![];
|
||||
let mut t_de = vec![];
|
||||
let mut t_cmaes = vec![];
|
||||
let mut t_ipop = vec![];
|
||||
|
||||
for seed in 0..SEEDS {
|
||||
// Differential Evolution
|
||||
let t = Instant::now();
|
||||
let mut de = DifferentialEvolution::new(
|
||||
DifferentialEvolutionConfig {
|
||||
population_size: 30,
|
||||
generations: BUDGET / 30,
|
||||
differential_weight: 0.5,
|
||||
crossover_probability: 0.9,
|
||||
seed,
|
||||
},
|
||||
RealBounds::new(bounds.clone()),
|
||||
);
|
||||
let r = de.run(&MyProblem);
|
||||
t_de.push(t.elapsed().as_millis() as f64);
|
||||
best_de.push(r.best.unwrap().evaluation.objectives[0]);
|
||||
|
||||
// CMA-ES
|
||||
let t = Instant::now();
|
||||
let mut cma = CmaEs::new(
|
||||
CmaEsConfig {
|
||||
population_size: 12,
|
||||
generations: BUDGET / 12,
|
||||
initial_sigma: 1.0,
|
||||
eigen_decomposition_period: 1,
|
||||
initial_mean: None,
|
||||
seed,
|
||||
},
|
||||
RealBounds::new(bounds.clone()),
|
||||
);
|
||||
let r = cma.run(&MyProblem);
|
||||
t_cmaes.push(t.elapsed().as_millis() as f64);
|
||||
best_cmaes.push(r.best.unwrap().evaluation.objectives[0]);
|
||||
|
||||
// IPOP-CMA-ES
|
||||
let t = Instant::now();
|
||||
let mut ipop = IpopCmaEs::new(
|
||||
IpopCmaEsConfig {
|
||||
base: CmaEsConfig {
|
||||
population_size: 12,
|
||||
generations: BUDGET / 12 / 4,
|
||||
initial_sigma: 1.0,
|
||||
eigen_decomposition_period: 1,
|
||||
initial_mean: None,
|
||||
seed,
|
||||
},
|
||||
max_restarts: 3,
|
||||
population_factor: 2.0,
|
||||
seed,
|
||||
},
|
||||
RealBounds::new(bounds.clone()),
|
||||
);
|
||||
let r = ipop.run(&MyProblem);
|
||||
t_ipop.push(t.elapsed().as_millis() as f64);
|
||||
best_ipop.push(r.best.unwrap().evaluation.objectives[0]);
|
||||
}
|
||||
|
||||
println!("{:<12} {:>14} {:>10}", "algorithm", "best f (mean±std)", "ms");
|
||||
print_row("DE", &best_de, &t_de);
|
||||
print_row("CMA-ES", &best_cmaes, &t_cmaes);
|
||||
print_row("IPOP-CMA-ES", &best_ipop, &t_ipop);
|
||||
}
|
||||
|
||||
fn print_row(name: &str, values: &[f64], times: &[f64]) {
|
||||
let (m, s) = mean_std(values);
|
||||
let (t, _) = mean_std(times);
|
||||
println!("{:<12} {:>10.3e} ± {:>5.2e} {:>6.0}", name, m, s, t);
|
||||
}
|
||||
|
||||
fn mean_std(xs: &[f64]) -> (f64, f64) {
|
||||
let n = xs.len() as f64;
|
||||
let m = xs.iter().sum::<f64>() / n;
|
||||
let v = xs.iter().map(|x| (x - m).powi(2)).sum::<f64>() / n;
|
||||
(m, v.sqrt())
|
||||
}
|
||||
```
|
||||
|
||||
## What to record
|
||||
|
||||
- **`best.evaluation.objectives[0]`** for single-objective.
|
||||
- **`hypervolume_2d(&result.pareto_front, &space, ref_point)`** for
|
||||
2-objective.
|
||||
- **`spacing(&result.pareto_front, &space)`** for front uniformity.
|
||||
- **`result.evaluations`** to cross-check that every algorithm got
|
||||
the same evaluation budget.
|
||||
- Wall-clock `Instant::now()` deltas for runtime comparison.
|
||||
|
||||
## Pitfalls
|
||||
|
||||
- **Population size matters.** Different algorithms have very
|
||||
different sweet spots. Don't just give them all the same
|
||||
population — the README's algorithm pages note typical defaults.
|
||||
- **Different algorithms count "generations" differently.** What
|
||||
matters is the total `evaluations` count. Set
|
||||
`generations = BUDGET / population_size` to match across
|
||||
algorithms (with caveats for steady-state algorithms like SMS-EMOA
|
||||
that evaluate one offspring per generation).
|
||||
- **One seed is not a comparison.** Always run ≥ 5 seeds; ≥ 10 is
|
||||
better. Single-seed comparisons are noise.
|
||||
- **The harness in `examples/compare.rs` is the canonical version.**
|
||||
When in doubt, copy from there.
|
||||
|
||||
[`Problem`]: https://docs.rs/heuropt/latest/heuropt/core/problem/trait.Problem.html
|
||||
@@ -0,0 +1,126 @@
|
||||
# Constrain your search with `Repair`
|
||||
|
||||
heuropt models constraints with a single `constraint_violation` scalar
|
||||
on each `Evaluation`. That works for soft penalties. When constraints
|
||||
are *hard* and the search keeps generating infeasible decisions, the
|
||||
better pattern is **repair**: project each candidate back into the
|
||||
feasible region every time it leaves.
|
||||
|
||||
The [`Repair<D>`] trait is the abstraction. Two impls ship in the box;
|
||||
you can write your own for arbitrary geometry.
|
||||
|
||||
## Built-in: `ClampToBounds`
|
||||
|
||||
For per-axis box constraints (`lo ≤ xᵢ ≤ hi`), pair `ClampToBounds`
|
||||
with any `Variation` to get a bounds-aware variant for free.
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
let bounds = vec![(-5.0, 5.0); 3];
|
||||
|
||||
// Without bounds, GaussianMutation can step outside the search box.
|
||||
// ClampToBounds projects each variable back in.
|
||||
let mut sigma = GaussianMutation { sigma: 0.5 };
|
||||
let mut clamp = ClampToBounds::new(bounds.clone());
|
||||
|
||||
let mut rng = rng_from_seed(42);
|
||||
let parent = vec![4.9, -4.9, 0.0];
|
||||
let mut child = sigma.vary(std::slice::from_ref(&parent), &mut rng).pop().unwrap();
|
||||
clamp.repair(&mut child);
|
||||
// every entry of `child` is now within [-5, 5].
|
||||
```
|
||||
|
||||
`ClampToBounds` is idempotent: applying it twice is the same as
|
||||
applying it once.
|
||||
|
||||
For most real problems you'd just use [`BoundedGaussianMutation`]
|
||||
which combines both in one operator.
|
||||
|
||||
## Built-in: `ProjectToSimplex`
|
||||
|
||||
For *budget* constraints — "the components must sum to a fixed
|
||||
total and be non-negative" — `ProjectToSimplex` projects onto the
|
||||
probability simplex (or any scaled simplex).
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
let mut proj = ProjectToSimplex::new(1.0); // probability simplex
|
||||
let mut x = vec![0.6, 0.5, -0.1, 0.3]; // sum 1.3, one negative
|
||||
proj.repair(&mut x);
|
||||
// x now sums to 1.0 and every entry is ≥ 0.
|
||||
let s: f64 = x.iter().sum();
|
||||
debug_assert!((s - 1.0).abs() < 1e-12);
|
||||
debug_assert!(x.iter().all(|&v| v >= 0.0));
|
||||
```
|
||||
|
||||
Use this for portfolio / resource-allocation problems where the
|
||||
decision is a vector of weights that must sum to a budget.
|
||||
|
||||
## Custom repair
|
||||
|
||||
Anything that takes a `&mut Vec<f64>` (or any `&mut D` for your
|
||||
custom decision type) and returns a feasible version is a valid
|
||||
`Repair`. Implement the trait directly:
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
/// Force the largest variable to be at least `min_largest`.
|
||||
struct AtLeastOneActive { min_largest: f64 }
|
||||
|
||||
impl Repair<Vec<f64>> for AtLeastOneActive {
|
||||
fn repair(&mut self, x: &mut Vec<f64>) {
|
||||
let max_idx = x.iter()
|
||||
.enumerate()
|
||||
.fold(0, |best, (i, &v)| {
|
||||
if v > x[best] { i } else { best }
|
||||
});
|
||||
if x[max_idx] < self.min_largest {
|
||||
x[max_idx] = self.min_largest;
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Stochastic-ranking selection
|
||||
|
||||
When the feasible region is *narrow* — most of the search space is
|
||||
infeasible — the strict "feasibles always beat infeasibles" rule
|
||||
traps the search outside it. Runarsson & Yao's stochastic ranking
|
||||
breaks the trap by, on each pairwise comparison, using a probabilistic
|
||||
"compare by objective" instead of "compare by feasibility" with a
|
||||
small probability `pf`:
|
||||
|
||||
```rust,ignore
|
||||
use heuropt::selection::tournament::stochastic_ranking_select;
|
||||
|
||||
let picks = stochastic_ranking_select(
|
||||
&population,
|
||||
&objectives,
|
||||
0.45, // pf — Runarsson & Yao's canonical value
|
||||
count,
|
||||
&mut rng,
|
||||
);
|
||||
```
|
||||
|
||||
This is a drop-in replacement for `tournament_select_single_objective`
|
||||
in your custom optimizer or in a forked algorithm.
|
||||
|
||||
## When to use which
|
||||
|
||||
| Situation | Use |
|
||||
|---|---|
|
||||
| Box constraints | [`BoundedGaussianMutation`] (built-in mutation) |
|
||||
| Manual repair after any mutation | [`ClampToBounds`] |
|
||||
| Budget / probability-simplex constraints | [`ProjectToSimplex`] |
|
||||
| Custom geometric constraints | Your own `Repair` impl |
|
||||
| Narrow feasible region, frequent infeasibility | [`stochastic_ranking_select`] |
|
||||
| Soft penalty, mostly feasible search | Set `constraint_violation` and let default tournament handle it |
|
||||
|
||||
[`Repair<D>`]: https://docs.rs/heuropt/latest/heuropt/traits/trait.Repair.html
|
||||
[`ClampToBounds`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.ClampToBounds.html
|
||||
[`ProjectToSimplex`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.ProjectToSimplex.html
|
||||
[`BoundedGaussianMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.BoundedGaussianMutation.html
|
||||
[`stochastic_ranking_select`]: https://docs.rs/heuropt/latest/heuropt/selection/tournament/fn.stochastic_ranking_select.html
|
||||
@@ -0,0 +1,146 @@
|
||||
# Write your own algorithm
|
||||
|
||||
Implement [`Optimizer<P>`] and you're done. There are no other traits
|
||||
to think about, no internal hooks to register. The example walks
|
||||
through a tiny hill-climber that reads almost identically to the
|
||||
canonical pseudocode.
|
||||
|
||||
## The trait
|
||||
|
||||
```rust,ignore
|
||||
pub trait Optimizer<P>
|
||||
where
|
||||
P: Problem,
|
||||
{
|
||||
fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision>;
|
||||
}
|
||||
```
|
||||
|
||||
That's it. You own your config, your RNG, your main loop, and your
|
||||
`OptimizationResult` construction.
|
||||
|
||||
## A minimal hill-climber
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
pub struct MyHillClimber<I, V> {
|
||||
pub iterations: usize,
|
||||
pub seed: u64,
|
||||
pub initializer: I,
|
||||
pub variation: V,
|
||||
}
|
||||
|
||||
impl<P, I, V> Optimizer<P> for MyHillClimber<I, V>
|
||||
where
|
||||
P: Problem,
|
||||
P::Decision: Clone,
|
||||
I: Initializer<P::Decision>,
|
||||
V: Variation<P::Decision>,
|
||||
{
|
||||
fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision> {
|
||||
let mut rng = rng_from_seed(self.seed);
|
||||
let objectives = problem.objectives();
|
||||
assert!(objectives.is_single_objective(), "MyHillClimber is single-objective only");
|
||||
|
||||
// Start with one initial decision.
|
||||
let init_decisions = self.initializer.initialize(1, &mut rng);
|
||||
let init = init_decisions.into_iter().next().unwrap();
|
||||
let mut current = Candidate::new(init.clone(), problem.evaluate(&init));
|
||||
let mut evaluations: usize = 1;
|
||||
|
||||
for _ in 0..self.iterations {
|
||||
let children = self.variation.vary(std::slice::from_ref(¤t.decision), &mut rng);
|
||||
for child_decision in children {
|
||||
let child_eval = problem.evaluate(&child_decision);
|
||||
evaluations += 1;
|
||||
let child = Candidate::new(child_decision, child_eval);
|
||||
if better(&child.evaluation, ¤t.evaluation, &objectives) {
|
||||
current = child;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let pareto_front = vec![current.clone()];
|
||||
let best = Some(current.clone());
|
||||
OptimizationResult::new(
|
||||
Population::new(vec![current]),
|
||||
pareto_front,
|
||||
best,
|
||||
evaluations,
|
||||
self.iterations,
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
fn better(a: &Evaluation, b: &Evaluation, objectives: &ObjectiveSpace) -> bool {
|
||||
let am = objectives.as_minimization(&a.objectives);
|
||||
let bm = objectives.as_minimization(&b.objectives);
|
||||
am[0] < bm[0]
|
||||
}
|
||||
```
|
||||
|
||||
## Things to notice
|
||||
|
||||
- **`Rng` is one concrete type.** No generics — call
|
||||
[`rng_from_seed`] and pass `&mut rng` everywhere it's needed.
|
||||
- **`Initializer<D>`** sources the starting point(s).
|
||||
- **`Variation<D>`** generates children from parents. For the
|
||||
hill-climber it's called with one parent.
|
||||
- **`OptimizationResult`** carries the final population, the Pareto
|
||||
front (just the best for single-objective), the best candidate,
|
||||
the total evaluations, and the iteration count.
|
||||
- **`as_minimization`** flips maximize-axis values so your
|
||||
comparison logic only ever needs to deal with "lower is better."
|
||||
|
||||
## Adding parallel evaluation
|
||||
|
||||
If your algorithm batch-evaluates candidates per generation, use the
|
||||
crate's internal helper. From inside heuropt source you can call
|
||||
`evaluate_batch(problem, decisions)`; from outside you'd use rayon
|
||||
directly behind a feature flag, the same way the built-in algorithms
|
||||
do.
|
||||
|
||||
```rust,ignore
|
||||
#[cfg(feature = "parallel")]
|
||||
fn batch_eval<P>(problem: &P, decisions: Vec<P::Decision>) -> Vec<Candidate<P::Decision>>
|
||||
where P: Problem + Sync, P::Decision: Send,
|
||||
{
|
||||
use rayon::prelude::*;
|
||||
decisions.into_par_iter()
|
||||
.map(|d| Candidate::new(d.clone(), problem.evaluate(&d)))
|
||||
.collect()
|
||||
}
|
||||
|
||||
#[cfg(not(feature = "parallel"))]
|
||||
fn batch_eval<P>(problem: &P, decisions: Vec<P::Decision>) -> Vec<Candidate<P::Decision>>
|
||||
where P: Problem,
|
||||
{
|
||||
decisions.into_iter()
|
||||
.map(|d| Candidate::new(d.clone(), problem.evaluate(&d)))
|
||||
.collect()
|
||||
}
|
||||
```
|
||||
|
||||
To stay bit-identical between serial and parallel modes, keep the
|
||||
RNG and selection on the main thread; only the *evaluations* run in
|
||||
parallel.
|
||||
|
||||
## What's *not* in the trait
|
||||
|
||||
- **No iteration / step API.** The optimizer owns its loop.
|
||||
- **No callbacks.** A future minor release may add an observer hook;
|
||||
for now you'd run the algorithm to completion and process the
|
||||
result.
|
||||
- **No error type.** Invalid configuration panics with a clear
|
||||
message; this matches the style of the built-in algorithms.
|
||||
- **No async.** `evaluate` is synchronous; for async work, drive it
|
||||
on a tokio runtime around the optimizer loop yourself.
|
||||
|
||||
The smallness is the point: you should be able to read a built-in
|
||||
algorithm and write your own in an afternoon. See
|
||||
`examples/custom_optimizer.rs` for a slightly more polished version
|
||||
of the hill-climber above.
|
||||
|
||||
[`Optimizer<P>`]: https://docs.rs/heuropt/latest/heuropt/traits/trait.Optimizer.html
|
||||
[`rng_from_seed`]: https://docs.rs/heuropt/latest/heuropt/core/rng/fn.rng_from_seed.html
|
||||
@@ -0,0 +1,164 @@
|
||||
# Tune a model with expensive evaluations
|
||||
|
||||
Population-based EAs throw thousands of evaluations at a problem. If
|
||||
each evaluation costs a minute (a model training run, a CFD solve, a
|
||||
real-world measurement) you can't afford that. heuropt has three
|
||||
algorithms aimed at this regime.
|
||||
|
||||
| Algorithm | Surrogate | Best for |
|
||||
|---|---|---|
|
||||
| [`BayesianOpt`] | Gaussian process + Expected Improvement | The textbook choice; needs kernel tuning to shine |
|
||||
| [`Tpe`] | Kernel-density estimate of good vs bad points | Cheaper per step; more robust without tuning |
|
||||
| [`Hyperband`] | (none — it's a multi-fidelity scheduler) | When each eval has a tunable budget (epochs, MC samples) |
|
||||
|
||||
## When each is right
|
||||
|
||||
- **Black-box, fixed cost per eval, smooth-ish landscape** → BO.
|
||||
- **Black-box, fixed cost per eval, no time to tune the surrogate** → TPE.
|
||||
- **Each eval has a tunable fidelity** → Hyperband.
|
||||
|
||||
## Bayesian Optimization
|
||||
|
||||
A worked example with a synthetic 5-D problem and a 60-evaluation
|
||||
budget — same configuration the `compare` harness uses.
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
struct Rosenbrock5D;
|
||||
impl Problem for Rosenbrock5D {
|
||||
type Decision = Vec<f64>;
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
}
|
||||
fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
let f: f64 = x.windows(2).map(|w|
|
||||
100.0 * (w[1] - w[0].powi(2)).powi(2) + (1.0 - w[0]).powi(2)
|
||||
).sum();
|
||||
Evaluation::new(vec![f])
|
||||
}
|
||||
}
|
||||
|
||||
let bounds = vec![(-2.048_f64, 2.048_f64); 5];
|
||||
let mut opt = BayesianOpt::new(
|
||||
BayesianOptConfig {
|
||||
evaluations: 60,
|
||||
initial_samples: 10,
|
||||
length_scale: 1.0,
|
||||
signal_variance: 1.0,
|
||||
noise_variance: 1e-6,
|
||||
seed: 42,
|
||||
},
|
||||
RealBounds::new(bounds),
|
||||
);
|
||||
let r = opt.run(&Rosenbrock5D);
|
||||
println!("best f after 60 evals: {}", r.best.unwrap().evaluation.objectives[0]);
|
||||
```
|
||||
|
||||
> **Honest disclosure.** On the comparison harness this default
|
||||
> configuration produces **f ≈ 3170 ± 2920** on Rosenbrock 5-D — well
|
||||
> below what a tuned BO can do. The default RBF kernel without
|
||||
> per-problem hyperparameter tuning is the limitation. For real
|
||||
> workloads, consider:
|
||||
>
|
||||
> - More evaluations (200+ instead of 60).
|
||||
> - Tuning `length_scale` to a known scale of your problem
|
||||
> (lower for high-frequency landscapes, higher for smooth ones).
|
||||
> - TPE instead of BO if you don't want to tune the kernel.
|
||||
|
||||
## Tree-structured Parzen Estimator
|
||||
|
||||
TPE keeps two density estimates — `l(x)` over historical good points
|
||||
and `g(x)` over the rest — and picks new candidates that maximize the
|
||||
ratio. Cheaper per step than a GP and famously robust without
|
||||
hand-tuning.
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
# struct Rosenbrock5D;
|
||||
# impl Problem for Rosenbrock5D {
|
||||
# type Decision = Vec<f64>;
|
||||
# fn objectives(&self) -> ObjectiveSpace { ObjectiveSpace::new(vec![Objective::minimize("f")]) }
|
||||
# fn evaluate(&self, _x: &Vec<f64>) -> Evaluation { Evaluation::new(vec![0.0]) }
|
||||
# }
|
||||
let bounds = vec![(-2.048_f64, 2.048_f64); 5];
|
||||
let mut opt = Tpe::new(
|
||||
TpeConfig {
|
||||
evaluations: 60,
|
||||
initial_samples: 10,
|
||||
gamma: 0.25,
|
||||
candidates_per_step: 24,
|
||||
bandwidth_factor: 1.06,
|
||||
seed: 42,
|
||||
},
|
||||
RealBounds::new(bounds),
|
||||
);
|
||||
let _r = opt.run(&Rosenbrock5D);
|
||||
```
|
||||
|
||||
`gamma` is the fraction of best points used as `l(x)`; `0.25` is the
|
||||
canonical Bergstra value.
|
||||
|
||||
## Hyperband
|
||||
|
||||
[`Hyperband`] needs your problem to implement [`PartialProblem`] —
|
||||
that is, you can evaluate at a tunable fidelity (e.g. number of
|
||||
training epochs). The algorithm schedules many cheap-fidelity runs
|
||||
and promotes only the survivors to higher fidelity.
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
use heuropt::core::partial_problem::PartialProblem;
|
||||
|
||||
struct ModelTuning;
|
||||
impl Problem for ModelTuning {
|
||||
type Decision = Vec<f64>;
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("val_loss")])
|
||||
}
|
||||
fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
// Full-fidelity eval = train at max_epochs.
|
||||
self.evaluate_at_budget(x, 100.0)
|
||||
}
|
||||
}
|
||||
impl PartialProblem for ModelTuning {
|
||||
fn evaluate_at_budget(&self, x: &Vec<f64>, budget: f64) -> Evaluation {
|
||||
// Replace with: train your model for `budget` epochs, return val_loss.
|
||||
// For demo, pretend more budget = lower noisy loss.
|
||||
let lr = x[0];
|
||||
let wd = x[1];
|
||||
let loss = (lr - 0.001).powi(2) + (wd - 1e-4).powi(2)
|
||||
+ 1.0 / (budget + 1.0);
|
||||
Evaluation::new(vec![loss])
|
||||
}
|
||||
}
|
||||
|
||||
let bounds = vec![(1e-5_f64, 1e-1), (1e-6_f64, 1e-2)];
|
||||
let mut hyperband = Hyperband::new(
|
||||
HyperbandConfig {
|
||||
max_budget: 100.0,
|
||||
eta: 3.0,
|
||||
seed: 42,
|
||||
},
|
||||
RealBounds::new(bounds),
|
||||
);
|
||||
let _r = hyperband.run(&ModelTuning);
|
||||
```
|
||||
|
||||
`max_budget` is the most epochs (or whatever your fidelity unit is)
|
||||
you'd ever spend on a single config. `eta` controls how aggressive
|
||||
the elimination is — `3.0` is the classic value; higher means more
|
||||
aggressive culling.
|
||||
|
||||
## Strategy: combining surrogate + multi-fidelity
|
||||
|
||||
The state of the art (BOHB) combines BO with Hyperband: TPE picks the
|
||||
configurations Hyperband then evaluates at increasing fidelity.
|
||||
heuropt doesn't ship a unified BOHB but the building blocks are
|
||||
there — wrap your `PartialProblem` with a TPE-driven sampler and
|
||||
feed the picks into `Hyperband`. PRs welcome.
|
||||
|
||||
[`BayesianOpt`]: https://docs.rs/heuropt/latest/heuropt/algorithms/bayesian_opt/struct.BayesianOpt.html
|
||||
[`Tpe`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tpe/struct.Tpe.html
|
||||
[`Hyperband`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hyperband/struct.Hyperband.html
|
||||
[`PartialProblem`]: https://docs.rs/heuropt/latest/heuropt/core/partial_problem/trait.PartialProblem.html
|
||||
@@ -0,0 +1,127 @@
|
||||
# Parallelize evaluation with rayon
|
||||
|
||||
If a single call to your `evaluate` takes more than ~50 µs, enabling
|
||||
the `parallel` feature usually pays for itself immediately on
|
||||
population-based algorithms. Each generation evaluates an entire
|
||||
population, and rayon parallelizes that batch.
|
||||
|
||||
## Enable the feature
|
||||
|
||||
```toml
|
||||
[dependencies]
|
||||
heuropt = { version = "0.5", features = ["parallel"] }
|
||||
```
|
||||
|
||||
There's nothing else to opt into in your code. The
|
||||
population-evaluation helper is feature-gated; with `parallel` on it
|
||||
uses `rayon::into_par_iter` internally, with `parallel` off it falls
|
||||
back to plain `into_iter`.
|
||||
|
||||
## Determinism still holds
|
||||
|
||||
Seeded runs are bit-identical between the serial and parallel modes.
|
||||
The trick is that population members are evaluated in parallel but
|
||||
*assembled* back into the same order. Variation, selection, and the
|
||||
RNG are all driven by the main thread, so seed-stability tests still
|
||||
pass.
|
||||
|
||||
## Which algorithms benefit
|
||||
|
||||
Algorithms with a per-generation `evaluate_batch`:
|
||||
|
||||
- [`RandomSearch`], [`Nsga2`], [`Nsga3`], [`Spea2`], [`Moead`],
|
||||
[`Mopso`], [`Ibea`], [`SmsEmoa`], [`HypE`], [`PesaII`],
|
||||
[`EpsilonMoea`], [`AgeMoea`], [`Knea`], [`Grea`], [`Rvea`].
|
||||
- [`DifferentialEvolution`] and [`GeneticAlgorithm`] benefit on the
|
||||
initial population and offspring batches.
|
||||
|
||||
Steady-state algorithms ([`Paes`], [`SimulatedAnnealing`],
|
||||
[`HillClimber`], [`OnePlusOneEs`]) only evaluate one or a few
|
||||
candidates per iteration, so the parallel feature gives them
|
||||
nothing — leave it off if those are your primary optimizers.
|
||||
|
||||
## Worked example
|
||||
|
||||
The Sphere problem is too cheap to actually benefit from parallelism
|
||||
— this example just shows the shape. In real workloads `evaluate` is
|
||||
the expensive bit (a simulation, a model fit, an HTTP call).
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
struct ExpensiveSphere;
|
||||
impl Problem for ExpensiveSphere {
|
||||
type Decision = Vec<f64>;
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
}
|
||||
fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
// Pretend this is a 5 ms simulation.
|
||||
std::thread::sleep(std::time::Duration::from_millis(5));
|
||||
Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
}
|
||||
}
|
||||
|
||||
fn main() {
|
||||
let bounds = vec![(-1.0_f64, 1.0_f64); 5];
|
||||
let mut opt = DifferentialEvolution::new(
|
||||
DifferentialEvolutionConfig {
|
||||
population_size: 16,
|
||||
generations: 50,
|
||||
differential_weight: 0.5,
|
||||
crossover_probability: 0.9,
|
||||
seed: 42,
|
||||
},
|
||||
RealBounds::new(bounds),
|
||||
);
|
||||
let r = opt.run(&ExpensiveSphere);
|
||||
println!("best f = {}", r.best.unwrap().evaluation.objectives[0]);
|
||||
}
|
||||
```
|
||||
|
||||
With the `parallel` feature on, each generation's 16 evaluations run
|
||||
across rayon's worker threads. On a 16-core machine the wall-clock
|
||||
cost per generation drops from `16 × 5 ms = 80 ms` to roughly
|
||||
`5 ms + scheduling overhead`.
|
||||
|
||||
## Sizing your thread pool
|
||||
|
||||
heuropt uses rayon's global thread pool. Override the size with:
|
||||
|
||||
```rust,ignore
|
||||
rayon::ThreadPoolBuilder::new().num_threads(8).build_global().unwrap();
|
||||
```
|
||||
|
||||
Run this **before** any heuropt call, or use rayon's `install` API
|
||||
to scope it.
|
||||
|
||||
## When parallelism *doesn't* help
|
||||
|
||||
- Your `evaluate` is sub-microsecond (Sphere, Rastrigin, Ackley
|
||||
unweighted) — the rayon scheduling overhead exceeds the work.
|
||||
- You're already running multiple seeds in parallel at the harness
|
||||
level (see [Compare two algorithms](./compare.md)). Stacking
|
||||
parallelism rarely helps.
|
||||
- The algorithm is steady-state (Paes, SA, hill climber).
|
||||
|
||||
[`RandomSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/random_search/struct.RandomSearch.html
|
||||
[`Nsga2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga2/struct.Nsga2.html
|
||||
[`Nsga3`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga3/struct.Nsga3.html
|
||||
[`Spea2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/spea2/struct.Spea2.html
|
||||
[`Moead`]: https://docs.rs/heuropt/latest/heuropt/algorithms/moead/struct.Moead.html
|
||||
[`Mopso`]: https://docs.rs/heuropt/latest/heuropt/algorithms/mopso/struct.Mopso.html
|
||||
[`Ibea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ibea/struct.Ibea.html
|
||||
[`SmsEmoa`]: https://docs.rs/heuropt/latest/heuropt/algorithms/sms_emoa/struct.SmsEmoa.html
|
||||
[`HypE`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hype/struct.Hype.html
|
||||
[`PesaII`]: https://docs.rs/heuropt/latest/heuropt/algorithms/pesa2/struct.PesaII.html
|
||||
[`EpsilonMoea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/epsilon_moea/struct.EpsilonMoea.html
|
||||
[`AgeMoea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/age_moea/struct.AgeMoea.html
|
||||
[`Knea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/knea/struct.Knea.html
|
||||
[`Grea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/grea/struct.Grea.html
|
||||
[`Rvea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/rvea/struct.Rvea.html
|
||||
[`DifferentialEvolution`]: https://docs.rs/heuropt/latest/heuropt/algorithms/differential_evolution/struct.DifferentialEvolution.html
|
||||
[`GeneticAlgorithm`]: https://docs.rs/heuropt/latest/heuropt/algorithms/genetic_algorithm/struct.GeneticAlgorithm.html
|
||||
[`Paes`]: https://docs.rs/heuropt/latest/heuropt/algorithms/paes/struct.Paes.html
|
||||
[`SimulatedAnnealing`]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
|
||||
[`HillClimber`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hill_climber/struct.HillClimber.html
|
||||
[`OnePlusOneEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/one_plus_one_es/struct.OnePlusOneEs.html
|
||||
@@ -0,0 +1,167 @@
|
||||
# Optimize a permutation (TSP-style)
|
||||
|
||||
When your decision is "an ordering" — visiting cities, scheduling
|
||||
jobs, routing — the natural representation is `Vec<usize>` and the
|
||||
specialized algorithm is [`AntColonyTsp`]. Generic alternatives are
|
||||
[`SimulatedAnnealing`] + [`SwapMutation`] for any permutation, and
|
||||
[`TabuSearch`] when you have a custom neighbor function.
|
||||
|
||||
## TSP with `AntColonyTsp`
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
struct Tsp {
|
||||
distances: Vec<Vec<f64>>,
|
||||
}
|
||||
|
||||
impl Problem for Tsp {
|
||||
type Decision = Vec<usize>;
|
||||
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("length")])
|
||||
}
|
||||
|
||||
fn evaluate(&self, tour: &Vec<usize>) -> Evaluation {
|
||||
let mut len = 0.0;
|
||||
for w in tour.windows(2) {
|
||||
len += self.distances[w[0]][w[1]];
|
||||
}
|
||||
len += self.distances[*tour.last().unwrap()][tour[0]];
|
||||
Evaluation::new(vec![len])
|
||||
}
|
||||
}
|
||||
|
||||
fn main() {
|
||||
// 5-city Euclidean instance
|
||||
let cities = vec![
|
||||
(0.0, 0.0),
|
||||
(1.0, 5.0),
|
||||
(5.0, 2.0),
|
||||
(6.0, 6.0),
|
||||
(8.0, 3.0),
|
||||
];
|
||||
let n = cities.len();
|
||||
let mut distances = vec![vec![0.0; n]; n];
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
let dx = cities[i].0 - cities[j].0;
|
||||
let dy = cities[i].1 - cities[j].1;
|
||||
distances[i][j] = (dx * dx + dy * dy).sqrt();
|
||||
}
|
||||
}
|
||||
let problem = Tsp { distances: distances.clone() };
|
||||
|
||||
let mut opt = AntColonyTsp::new(AntColonyTspConfig {
|
||||
ants: 20,
|
||||
iterations: 200,
|
||||
alpha: 1.0,
|
||||
beta: 5.0,
|
||||
evaporation: 0.5,
|
||||
deposit: 1.0,
|
||||
distances,
|
||||
seed: 42,
|
||||
});
|
||||
|
||||
let r = opt.run(&problem);
|
||||
let best = r.best.unwrap();
|
||||
println!("best tour length: {:.3}", best.evaluation.objectives[0]);
|
||||
println!("tour: {:?}", best.decision);
|
||||
}
|
||||
```
|
||||
|
||||
`alpha` weights pheromone influence and `beta` weights the
|
||||
heuristic (1 / distance). `evaporation` is the per-iteration decay
|
||||
of pheromone trails. The classic Dorigo paper uses `alpha = 1`,
|
||||
`beta = 2..5`, `evaporation = 0.1..0.5`.
|
||||
|
||||
## Generic permutation: SA + SwapMutation
|
||||
|
||||
Use this when your problem isn't TSP-shaped (no distance matrix
|
||||
makes sense) but you still want to optimize an ordering.
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
struct JobShop {
|
||||
process_times: Vec<f64>,
|
||||
}
|
||||
impl Problem for JobShop {
|
||||
type Decision = Vec<usize>;
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("makespan")])
|
||||
}
|
||||
fn evaluate(&self, schedule: &Vec<usize>) -> Evaluation {
|
||||
// Pretend cumulative weighted-completion-time. Replace with your real cost.
|
||||
let cost: f64 = schedule.iter().enumerate()
|
||||
.map(|(i, &job)| (i as f64 + 1.0) * self.process_times[job])
|
||||
.sum();
|
||||
Evaluation::new(vec![cost])
|
||||
}
|
||||
}
|
||||
|
||||
fn make_initial_perm(n: usize, seed: u64) -> Vec<usize> {
|
||||
use rand::seq::SliceRandom;
|
||||
let mut rng = rng_from_seed(seed);
|
||||
let mut perm: Vec<usize> = (0..n).collect();
|
||||
perm.shuffle(&mut rng);
|
||||
perm
|
||||
}
|
||||
|
||||
let times = vec![3.0, 1.5, 4.2, 2.7, 5.1];
|
||||
let problem = JobShop { process_times: times.clone() };
|
||||
|
||||
// SimulatedAnnealing needs a starting decision; pass a custom Initializer.
|
||||
struct OnePerm(Vec<usize>);
|
||||
impl Initializer<Vec<usize>> for OnePerm {
|
||||
fn initialize(&mut self, _size: usize, _rng: &mut Rng) -> Vec<Vec<usize>> {
|
||||
vec![self.0.clone()]
|
||||
}
|
||||
}
|
||||
|
||||
let mut opt = SimulatedAnnealing::new(
|
||||
SimulatedAnnealingConfig {
|
||||
iterations: 2000,
|
||||
initial_temperature: 5.0,
|
||||
final_temperature: 1e-3,
|
||||
seed: 7,
|
||||
},
|
||||
OnePerm(make_initial_perm(times.len(), 7)),
|
||||
SwapMutation,
|
||||
);
|
||||
let r = opt.run(&problem);
|
||||
let best = r.best.unwrap();
|
||||
println!("best makespan: {:.3}", best.evaluation.objectives[0]);
|
||||
println!("schedule: {:?}", best.decision);
|
||||
```
|
||||
|
||||
`SwapMutation` swaps two random indices in the permutation —
|
||||
preserves the "every element appears once" invariant for free.
|
||||
|
||||
## Custom neighborhoods: `TabuSearch`
|
||||
|
||||
When swap isn't the right move set (e.g., 2-opt for TSP, insert /
|
||||
shift for scheduling), use [`TabuSearch`] with your own neighbor
|
||||
function.
|
||||
|
||||
```rust,ignore
|
||||
use heuropt::prelude::*;
|
||||
let neighbors = |x: &Vec<usize>, _rng: &mut Rng| -> Vec<Vec<usize>> {
|
||||
// Generate all 2-opt neighbors of x.
|
||||
let mut out = Vec::new();
|
||||
for i in 0..x.len() {
|
||||
for j in (i + 2)..x.len() {
|
||||
let mut child = x.clone();
|
||||
child[i + 1..=j].reverse();
|
||||
out.push(child);
|
||||
}
|
||||
}
|
||||
out
|
||||
};
|
||||
// Pass `neighbors` to TabuSearch::new(...).
|
||||
```
|
||||
|
||||
[`AntColonyTsp`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ant_colony_tsp/struct.AntColonyTsp.html
|
||||
[`SimulatedAnnealing`]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
|
||||
[`SwapMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.SwapMutation.html
|
||||
[`TabuSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tabu_search/struct.TabuSearch.html
|
||||
@@ -0,0 +1,127 @@
|
||||
# Pick one answer off a Pareto front
|
||||
|
||||
A multi-objective optimizer hands you a *front* — a Pareto-optimal
|
||||
trade-off curve — not a single answer. Eventually you have to pick
|
||||
*one* point off it. There are several principled ways to do that;
|
||||
this recipe covers the most common: the **a-posteriori weighted
|
||||
decision rule**.
|
||||
|
||||
The pattern: optimize *without* baking your preferences into the
|
||||
search, then apply your preferences as a scoring function over the
|
||||
front.
|
||||
|
||||
This is exactly the pattern from `examples/jiggly_tuning.rs` (the
|
||||
USB-jiggler firmware tuning example).
|
||||
|
||||
## The shape
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
# struct Cost;
|
||||
# impl Problem for Cost {
|
||||
# type Decision = Vec<f64>;
|
||||
# fn objectives(&self) -> ObjectiveSpace {
|
||||
# ObjectiveSpace::new(vec![Objective::minimize("a"), Objective::minimize("b"), Objective::minimize("c")])
|
||||
# }
|
||||
# fn evaluate(&self, _x: &Vec<f64>) -> Evaluation { Evaluation::new(vec![0.0,0.0,0.0]) }
|
||||
# }
|
||||
|
||||
let problem = Cost;
|
||||
let mut opt = Nsga2::new(
|
||||
Nsga2Config { population_size: 100, generations: 200, seed: 42 },
|
||||
RealBounds::new(vec![(-1.0, 1.0); 4]),
|
||||
CompositeVariation {
|
||||
crossover: SimulatedBinaryCrossover::new(vec![(-1.0, 1.0); 4], 15.0, 0.5),
|
||||
mutation: PolynomialMutation::new(vec![(-1.0, 1.0); 4], 20.0, 1.0),
|
||||
},
|
||||
);
|
||||
let result = opt.run(&problem);
|
||||
|
||||
// 1. Get the Pareto front.
|
||||
let front = &result.pareto_front;
|
||||
|
||||
// 2. Define your preferences as a scoring function over (oriented)
|
||||
// objective values. Lower score = preferred.
|
||||
let space = problem.objectives();
|
||||
let weights = [1.0, 2.0, 0.5];
|
||||
|
||||
let scored: Vec<(f64, &Candidate<Vec<f64>>)> = front.iter()
|
||||
.map(|c| {
|
||||
let oriented = space.as_minimization(&c.evaluation.objectives);
|
||||
let score: f64 = oriented.iter().zip(&weights)
|
||||
.map(|(v, w)| v * w)
|
||||
.sum();
|
||||
(score, c)
|
||||
})
|
||||
.collect();
|
||||
|
||||
// 3. Pick the lowest-scoring point.
|
||||
let best = scored.iter()
|
||||
.min_by(|a, b| a.0.partial_cmp(&b.0).unwrap())
|
||||
.unwrap();
|
||||
|
||||
println!("picked: {:?} with weighted score {:.3}",
|
||||
best.1.evaluation.objectives, best.0);
|
||||
```
|
||||
|
||||
`as_minimization` returns the objective vector with maximized axes
|
||||
flipped to negative — so a single set of *positive* weights does
|
||||
the right thing whether each axis is min or max.
|
||||
|
||||
## Why a-posteriori vs a-priori weighting
|
||||
|
||||
If you know your weights up front, you could just optimize the
|
||||
weighted sum directly with a single-objective algorithm. Why bother
|
||||
with the multi-objective dance?
|
||||
|
||||
Two reasons:
|
||||
|
||||
1. **Weighted sum can't reach concave parts of the Pareto front.**
|
||||
Any single-objective optimization with a linear scalarization
|
||||
converges to a point at the boundary of the convex hull. Concave
|
||||
front segments are unreachable. The multi-objective optimizer
|
||||
finds them.
|
||||
2. **Weights are usually wrong on the first try.** Optimizing the
|
||||
front first lets you see what's actually possible before deciding
|
||||
how much each axis is worth. Run once, look at the trade-offs,
|
||||
adjust weights.
|
||||
|
||||
## Penalty terms beyond linear weights
|
||||
|
||||
The jiggly example also adds a *hinge penalty* — a term that's zero
|
||||
inside an acceptable region and grows quadratically once you exceed
|
||||
some hard cap. Useful when one axis is "soft up to X, hard cap at Y":
|
||||
|
||||
```rust,no_run
|
||||
fn hinge(x: f64, soft_cap: f64, hard_cap: f64) -> f64 {
|
||||
if x <= soft_cap { 0.0 }
|
||||
else if x >= hard_cap { f64::INFINITY }
|
||||
else {
|
||||
let t = (x - soft_cap) / (hard_cap - soft_cap);
|
||||
100.0 * t * t
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Compose linear weights + hinge penalties and you have a flexible
|
||||
scoring function over the front without re-running the optimizer.
|
||||
|
||||
## Other strategies
|
||||
|
||||
- **Knee point.** Pick the point where small gains in one axis cost
|
||||
large losses in another — the "elbow" of the trade-off curve.
|
||||
[`Knea`] explicitly biases the search toward knees during the run.
|
||||
- **Reference-direction.** Pick the point closest to a desired
|
||||
trade-off direction (a unit vector in objective space).
|
||||
[`Moead`] / [`Nsga3`] use this internally during search; you can
|
||||
apply it post-hoc the same way.
|
||||
- **Random / interactive selection.** Show the front to a user
|
||||
(perhaps via a plotting library), let them pick.
|
||||
|
||||
The right pick depends on the problem; the front itself doesn't
|
||||
prescribe one.
|
||||
|
||||
[`Knea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/knea/struct.Knea.html
|
||||
[`Moead`]: https://docs.rs/heuropt/latest/heuropt/algorithms/moead/struct.Moead.html
|
||||
[`Nsga3`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga3/struct.Nsga3.html
|
||||
@@ -0,0 +1,244 @@
|
||||
# Defining a problem
|
||||
|
||||
Everything in heuropt starts with the [`Problem`] trait. This chapter
|
||||
walks through every shape it can take.
|
||||
|
||||
## The trait
|
||||
|
||||
```rust,ignore
|
||||
pub trait Problem {
|
||||
type Decision: Clone;
|
||||
fn objectives(&self) -> ObjectiveSpace;
|
||||
fn evaluate(&self, decision: &Self::Decision) -> Evaluation;
|
||||
}
|
||||
```
|
||||
|
||||
Three things you decide:
|
||||
|
||||
1. **`Decision`** — the type of the thing you're optimizing.
|
||||
`Vec<f64>` is by far the most common; `Vec<bool>` for binary
|
||||
search, `Vec<usize>` for permutations, your own struct for
|
||||
anything else.
|
||||
2. **`objectives`** — how many objectives you have, what they're
|
||||
called, and whether each is minimized or maximized. Returned as
|
||||
an [`ObjectiveSpace`].
|
||||
3. **`evaluate`** — given one decision, score it. Returns an
|
||||
[`Evaluation`] with a vector of objective values (and optionally
|
||||
a constraint-violation scalar).
|
||||
|
||||
`evaluate` takes `&self`, so caches and lookup tables are easy. It
|
||||
is called many thousands of times during a typical run, so keep it
|
||||
fast.
|
||||
|
||||
## Single-objective continuous
|
||||
|
||||
The Rosenbrock banana — minimize a smooth non-convex valley.
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
struct Rosenbrock;
|
||||
|
||||
impl Problem for Rosenbrock {
|
||||
type Decision = Vec<f64>;
|
||||
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
}
|
||||
|
||||
fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
let f: f64 = x.windows(2)
|
||||
.map(|w| 100.0 * (w[1] - w[0].powi(2)).powi(2) + (1.0 - w[0]).powi(2))
|
||||
.sum();
|
||||
Evaluation::new(vec![f])
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Multi-objective
|
||||
|
||||
ZDT1 — two objectives that conflict. The Pareto front is the set of
|
||||
non-dominated trade-offs.
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
struct Zdt1 { dim: usize }
|
||||
|
||||
impl Problem for Zdt1 {
|
||||
type Decision = Vec<f64>;
|
||||
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![
|
||||
Objective::minimize("f1"),
|
||||
Objective::minimize("f2"),
|
||||
])
|
||||
}
|
||||
|
||||
fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
let n = x.len() as f64;
|
||||
let f1 = x[0];
|
||||
let g = 1.0 + 9.0 * x[1..].iter().sum::<f64>() / (n - 1.0);
|
||||
let h = 1.0 - (f1 / g).sqrt();
|
||||
let f2 = g * h;
|
||||
Evaluation::new(vec![f1, f2])
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
For multi-objective problems, pick a Pareto-aware optimizer:
|
||||
[`Nsga2`] is the canonical default; [`Mopso`] often wins on
|
||||
smooth-front 2-objective problems; [`Ibea`] often wins on
|
||||
disconnected fronts. See [choosing-an-algorithm](./choosing-an-algorithm.md).
|
||||
|
||||
## Maximizing instead of minimizing
|
||||
|
||||
heuropt's internals normalize everything to minimization, but you
|
||||
declare your objective with the orientation that's natural for your
|
||||
problem. A scoring problem might want to maximize:
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
let space = ObjectiveSpace::new(vec![
|
||||
Objective::minimize("cost"),
|
||||
Objective::maximize("accuracy"),
|
||||
]);
|
||||
```
|
||||
|
||||
`Objective::maximize` is a convenience for `Direction::Maximize`. Mix
|
||||
freely; the Pareto-comparison machinery handles the orientation.
|
||||
|
||||
## Constraints
|
||||
|
||||
heuropt models constraints as a single non-negative scalar
|
||||
**`constraint_violation`** on each `Evaluation`. The convention:
|
||||
|
||||
- `0.0` (or negative) means **feasible**.
|
||||
- Any positive value means **infeasible**, and bigger numbers are
|
||||
worse violations.
|
||||
|
||||
Pareto-comparison and tournament-selection helpers prefer feasible
|
||||
candidates and break ties on the violation magnitude — so the rule
|
||||
"feasibility comes first" is enforced automatically.
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
struct Constrained;
|
||||
impl Problem for Constrained {
|
||||
type Decision = Vec<f64>;
|
||||
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
}
|
||||
|
||||
fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
let f: f64 = x.iter().map(|v| v * v).sum();
|
||||
|
||||
// Constraint: x[0] + x[1] >= 1. Violation = how much we miss it by.
|
||||
let g1 = (1.0 - (x[0] + x[1])).max(0.0);
|
||||
let total_violation: f64 = g1; // sum of max(0, gᵢ) for each constraint
|
||||
|
||||
Evaluation::constrained(vec![f], total_violation)
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
If your constraints are very tight and the search keeps hitting them,
|
||||
see [Constrain your search with `Repair`](./cookbook/constraints.md).
|
||||
|
||||
## Decision types beyond `Vec<f64>`
|
||||
|
||||
### Binary (`Vec<bool>`)
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
struct OneMax { bits: usize }
|
||||
impl Problem for OneMax {
|
||||
type Decision = Vec<bool>;
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::maximize("ones")])
|
||||
}
|
||||
fn evaluate(&self, x: &Vec<bool>) -> Evaluation {
|
||||
Evaluation::new(vec![x.iter().filter(|b| **b).count() as f64])
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
For `Vec<bool>` problems, [`Umda`] is a parameter-free EDA;
|
||||
[`GeneticAlgorithm`] with [`BitFlipMutation`] is the GA route.
|
||||
|
||||
### Permutations (`Vec<usize>`)
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
struct Tsp { distances: Vec<Vec<f64>> }
|
||||
impl Problem for Tsp {
|
||||
type Decision = Vec<usize>;
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("length")])
|
||||
}
|
||||
fn evaluate(&self, tour: &Vec<usize>) -> Evaluation {
|
||||
let mut len = 0.0;
|
||||
for w in tour.windows(2) {
|
||||
len += self.distances[w[0]][w[1]];
|
||||
}
|
||||
len += self.distances[*tour.last().unwrap()][tour[0]];
|
||||
Evaluation::new(vec![len])
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
For permutations, [`AntColonyTsp`] specializes on TSP-style problems;
|
||||
[`TabuSearch`] takes a user-supplied neighbor function for arbitrary
|
||||
discrete neighborhoods; [`SimulatedAnnealing`] with [`SwapMutation`]
|
||||
is the simplest baseline.
|
||||
|
||||
### Custom decision types
|
||||
|
||||
Any `Clone` type works. If you have a struct, just implement `Clone`
|
||||
and you can use it. You'll need to write your own `Variation` impl
|
||||
to mutate it; see [Write your own algorithm](./cookbook/custom-optimizer.md).
|
||||
|
||||
## What `Evaluation` carries
|
||||
|
||||
```rust,ignore
|
||||
pub struct Evaluation {
|
||||
pub objectives: Vec<f64>, // one entry per objective
|
||||
pub constraint_violation: f64, // 0.0 = feasible
|
||||
}
|
||||
```
|
||||
|
||||
That's it. Construct with [`Evaluation::new`] for unconstrained
|
||||
problems or [`Evaluation::constrained`] when you have a violation.
|
||||
|
||||
## Summary
|
||||
|
||||
- Implement [`Problem`] with your decision type.
|
||||
- Declare objectives via [`ObjectiveSpace`] (mix minimize/maximize
|
||||
freely).
|
||||
- Return an [`Evaluation`] from `evaluate`.
|
||||
- For constraints, set `constraint_violation > 0` for infeasible
|
||||
decisions; heuropt's selection helpers prefer feasibles
|
||||
automatically.
|
||||
|
||||
Next: [Choosing an algorithm](./choosing-an-algorithm.md) walks
|
||||
through the decision tree.
|
||||
|
||||
[`Problem`]: https://docs.rs/heuropt/latest/heuropt/core/problem/trait.Problem.html
|
||||
[`ObjectiveSpace`]: https://docs.rs/heuropt/latest/heuropt/core/objective/struct.ObjectiveSpace.html
|
||||
[`Evaluation`]: https://docs.rs/heuropt/latest/heuropt/core/evaluation/struct.Evaluation.html
|
||||
[`Evaluation::new`]: https://docs.rs/heuropt/latest/heuropt/core/evaluation/struct.Evaluation.html#method.new
|
||||
[`Evaluation::constrained`]: https://docs.rs/heuropt/latest/heuropt/core/evaluation/struct.Evaluation.html#method.constrained
|
||||
[`Nsga2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga2/struct.Nsga2.html
|
||||
[`Mopso`]: https://docs.rs/heuropt/latest/heuropt/algorithms/mopso/struct.Mopso.html
|
||||
[`Ibea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ibea/struct.Ibea.html
|
||||
[`Umda`]: https://docs.rs/heuropt/latest/heuropt/algorithms/umda/struct.Umda.html
|
||||
[`GeneticAlgorithm`]: https://docs.rs/heuropt/latest/heuropt/algorithms/genetic_algorithm/struct.GeneticAlgorithm.html
|
||||
[`BitFlipMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.BitFlipMutation.html
|
||||
[`AntColonyTsp`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ant_colony_tsp/struct.AntColonyTsp.html
|
||||
[`TabuSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tabu_search/struct.TabuSearch.html
|
||||
[`SimulatedAnnealing`]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
|
||||
[`SwapMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.SwapMutation.html
|
||||
@@ -0,0 +1,127 @@
|
||||
# Five-minute walkthrough
|
||||
|
||||
The shortest path from a fresh project to a working optimizer.
|
||||
|
||||
## 1. Add heuropt to your `Cargo.toml`
|
||||
|
||||
```toml
|
||||
[dependencies]
|
||||
heuropt = "0.5"
|
||||
```
|
||||
|
||||
The default feature set is small. Optional features:
|
||||
|
||||
- `parallel` — rayon-backed parallel population evaluation.
|
||||
- `serde` — `Serialize` / `Deserialize` derives on the core data
|
||||
types.
|
||||
|
||||
```toml
|
||||
heuropt = { version = "0.5", features = ["parallel"] }
|
||||
```
|
||||
|
||||
## 2. Define a problem
|
||||
|
||||
A problem is a struct that implements the [`Problem`] trait. You tell
|
||||
heuropt what kind of decision your problem takes (`Vec<f64>`,
|
||||
`Vec<bool>`, …), what objectives it has (minimize or maximize), and
|
||||
how to score one decision.
|
||||
|
||||
```rust,no_run
|
||||
use heuropt::prelude::*;
|
||||
|
||||
struct Sphere;
|
||||
|
||||
impl Problem for Sphere {
|
||||
type Decision = Vec<f64>;
|
||||
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
}
|
||||
|
||||
fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
let f: f64 = x.iter().map(|v| v * v).sum();
|
||||
Evaluation::new(vec![f])
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The Sphere function is a single-objective continuous problem: minimize
|
||||
`f(x) = Σ xᵢ²`. The optimum is `x = 0`, `f = 0`.
|
||||
|
||||
## 3. Pick an algorithm and run it
|
||||
|
||||
For a smooth single-objective continuous problem, [`CmaEs`] is a
|
||||
strong default. Configure it, build it, run it.
|
||||
|
||||
```rust,no_run
|
||||
# use heuropt::prelude::*;
|
||||
# struct Sphere;
|
||||
# impl Problem for Sphere {
|
||||
# type Decision = Vec<f64>;
|
||||
# fn objectives(&self) -> ObjectiveSpace {
|
||||
# ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
# }
|
||||
# fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
# Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
# }
|
||||
# }
|
||||
let bounds = RealBounds::new(vec![(-5.0, 5.0); 5]); // 5-dim search box
|
||||
|
||||
let mut opt = CmaEs::new(
|
||||
CmaEsConfig {
|
||||
population_size: 12,
|
||||
generations: 80,
|
||||
initial_sigma: 1.0,
|
||||
eigen_decomposition_period: 1,
|
||||
initial_mean: None,
|
||||
seed: 42,
|
||||
},
|
||||
bounds,
|
||||
);
|
||||
|
||||
let result = opt.run(&Sphere);
|
||||
|
||||
let best = result.best.expect("at least one feasible candidate");
|
||||
println!("best f = {:.3e} at x = {:?}", best.evaluation.objectives[0], best.decision);
|
||||
```
|
||||
|
||||
Run with `cargo run --release` — heuristic optimization is allergic
|
||||
to debug builds. Expect output like:
|
||||
|
||||
```text
|
||||
best f = 1.4e-29 at x = [-1.6e-15, 4.5e-16, ...]
|
||||
```
|
||||
|
||||
CMA-ES drops to machine epsilon on the Sphere in well under 80
|
||||
generations.
|
||||
|
||||
## 4. What just happened
|
||||
|
||||
- [`Problem`] is the **what** you're optimizing.
|
||||
- [`CmaEs`] (or any other optimizer) is the **how**.
|
||||
- [`CmaEsConfig`] is a plain public-field struct: there are no
|
||||
builders, no chained setters, just public fields you set
|
||||
directly.
|
||||
- [`Optimizer::run`] returns an [`OptimizationResult`] containing the
|
||||
full final `population`, the `pareto_front` (just the best for
|
||||
single-objective), the `best` candidate, the total `evaluations`,
|
||||
and the number of `generations`.
|
||||
|
||||
## 5. Where to go next
|
||||
|
||||
- **Multi-objective:** see [Defining a problem](./defining-problems.md)
|
||||
for how to express two or more objectives, and
|
||||
[Choosing an algorithm](./choosing-an-algorithm.md) for which
|
||||
optimizer fits.
|
||||
- **Want to know which algorithm to pick:** read the README's
|
||||
decision tree, or jump straight to the [choosing-an-algorithm](./choosing-an-algorithm.md)
|
||||
chapter for the long form.
|
||||
- **Production patterns:** the [cookbook](./cookbook.md) has recipes
|
||||
for parallelism, expensive evaluations, comparing algorithms, and
|
||||
more.
|
||||
|
||||
[`Problem`]: https://docs.rs/heuropt/latest/heuropt/core/problem/trait.Problem.html
|
||||
[`Optimizer::run`]: https://docs.rs/heuropt/latest/heuropt/traits/trait.Optimizer.html
|
||||
[`OptimizationResult`]: https://docs.rs/heuropt/latest/heuropt/core/result/struct.OptimizationResult.html
|
||||
[`CmaEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/cma_es/struct.CmaEs.html
|
||||
[`CmaEsConfig`]: https://docs.rs/heuropt/latest/heuropt/algorithms/cma_es/struct.CmaEsConfig.html
|
||||
@@ -0,0 +1,82 @@
|
||||
# Introduction
|
||||
|
||||
heuropt is a practical Rust toolkit for **heuristic optimization** — the
|
||||
art of searching for good answers when the problem is too gnarly to
|
||||
solve analytically.
|
||||
|
||||
The kinds of problems heuropt is built for:
|
||||
|
||||
- **Single-objective:** "find the parameters that minimize the loss of
|
||||
this model." Hyperparameter tuning. Curve fitting. Calibration.
|
||||
- **Multi-objective:** "find the trade-off curve between cost and
|
||||
accuracy." Engineering design. Portfolio optimization. Fleet
|
||||
scheduling.
|
||||
- **Many-objective (4+):** the same idea but with enough objectives
|
||||
that classical Pareto methods break down. Power-grid planning.
|
||||
Airfoil design. Multi-criteria recommendation.
|
||||
|
||||
If your problem is differentiable and convex, you don't need this
|
||||
crate — use a gradient solver. heuropt is for the *messy* problems:
|
||||
landscapes with lots of local minima, decisions that aren't continuous
|
||||
(permutations, bit vectors), or evaluations that are noisy / expensive
|
||||
/ black-box.
|
||||
|
||||
## Why heuropt
|
||||
|
||||
There are other Rust optimization crates and many more in Python (pymoo,
|
||||
hyperopt, optuna, DEAP). heuropt's design priorities:
|
||||
|
||||
1. **Approachable code.** No trait objects in the public API. No
|
||||
GATs, HRTBs, generic-RNG plumbing. A junior Rust engineer should
|
||||
be able to read `RandomSearch` and write a new optimizer by
|
||||
implementing only the `Optimizer<P>` trait.
|
||||
2. **One concrete RNG type.** Seeded determinism is a property tested
|
||||
across the crate; identical inputs always produce identical
|
||||
outputs.
|
||||
3. **Algorithms that work.** Every algorithm is benchmarked against
|
||||
the canonical test problems (ZDT, DTLZ, Rastrigin, Rosenbrock,
|
||||
Ackley) and the results are checked into [examples/compare-results.md](https://github.com/swaits/heuropt/blob/main/examples/compare-results.md)
|
||||
so you can see what each algorithm's strengths actually are.
|
||||
4. **Testing as a first-class concern.** 316+ unit / integration /
|
||||
property tests, eight cargo-fuzz targets in CI, gungraun
|
||||
instruction-count benchmarks. The fuzzers find real bugs and the
|
||||
property tests check actual invariants.
|
||||
|
||||
## What's in the box
|
||||
|
||||
heuropt v0.5 ships **35 algorithms** spanning:
|
||||
|
||||
- Single-objective continuous: `RandomSearch`, `HillClimber`,
|
||||
`OnePlusOneEs`, `SimulatedAnnealing`, `GeneticAlgorithm`,
|
||||
`ParticleSwarm`, `DifferentialEvolution`, `Tlbo`, `CmaEs`,
|
||||
`IpopCmaEs`, `SeparableNes`, `NelderMead`.
|
||||
- Single-objective other types: `Umda` (binary), `TabuSearch`
|
||||
(any), `AntColonyTsp` (permutation).
|
||||
- Multi-objective (2–3): `Paes`, `Nsga2`, `Spea2`, `Mopso`, `Ibea`,
|
||||
`SmsEmoa`, `HypE`, `EpsilonMoea`, `PesaII`, `AgeMoea`, `Knea`,
|
||||
`Moead`.
|
||||
- Many-objective (4+): `Nsga3`, `Rvea`, `Grea`.
|
||||
- Sample-efficient / multi-fidelity: `BayesianOpt`, `Tpe`,
|
||||
`Hyperband`.
|
||||
|
||||
Plus the operators (SBX, PolynomialMutation, BoundedGaussianMutation,
|
||||
LevyMutation, BitFlipMutation, SwapMutation, ClampToBounds,
|
||||
ProjectToSimplex), the metrics (hypervolume, spacing), and the Pareto
|
||||
utilities (dominance, fronts, crowding distance, Das–Dennis reference
|
||||
points, the `ParetoArchive`) that you'd expect.
|
||||
|
||||
## How to use this guide
|
||||
|
||||
If you're new to heuropt, read it linearly:
|
||||
|
||||
1. [Five-minute walkthrough](./getting-started.md) — install, define
|
||||
a problem, run an optimizer, look at the result.
|
||||
2. [Defining a problem](./defining-problems.md) — the `Problem`
|
||||
trait in depth: single- vs multi-objective, constraints, custom
|
||||
decision types.
|
||||
3. [Choosing an algorithm](./choosing-an-algorithm.md) — the
|
||||
decision tree, expanded with the reasoning behind each branch.
|
||||
|
||||
If you're already up and running, jump into the [cookbook](./cookbook.md)
|
||||
for recipes, or [comparison](./comparison.md) for how heuropt stacks
|
||||
up against other libraries.
|
||||
@@ -0,0 +1,69 @@
|
||||
# Migration guides
|
||||
|
||||
Per-release notes for upgrading between heuropt versions. Skip the
|
||||
sections that don't apply to your starting version.
|
||||
|
||||
## To 0.5
|
||||
|
||||
### From 0.4.x
|
||||
|
||||
**No public-API changes.** v0.5 is a documentation-and-polish release.
|
||||
Bumping `heuropt = "0.5"` in your Cargo.toml is enough.
|
||||
|
||||
What changed:
|
||||
|
||||
- Added a comprehensive mdbook user guide (this book).
|
||||
- Added runnable rustdoc examples on every public algorithm,
|
||||
operator, metric, and Pareto utility.
|
||||
- Added real-world `examples/portfolio.rs`,
|
||||
`examples/hyperparam_tuning.rs`, and `examples/scheduling.rs`.
|
||||
- Added `CONTRIBUTING.md`, `SECURITY.md`, `CODE_OF_CONDUCT.md`
|
||||
(Builder's Code of Conduct), GitHub issue templates, and PR
|
||||
template.
|
||||
|
||||
The full list is in CHANGELOG.md.
|
||||
|
||||
### From earlier than 0.4
|
||||
|
||||
If you're coming from 0.3.x or earlier, also read the older sections
|
||||
below.
|
||||
|
||||
## To 0.4
|
||||
|
||||
### From 0.3.x
|
||||
|
||||
**No public-API changes.** v0.4 was a testing-infrastructure
|
||||
expansion + perf pass. Same `cargo update` story.
|
||||
|
||||
The compare-harness wall-clock got 3.27× faster on v0.4 with
|
||||
bit-identical quality metrics, so any benchmark numbers you have
|
||||
from v0.3 are still numerically accurate but will run faster.
|
||||
|
||||
## To 0.3
|
||||
|
||||
### From 0.2.x
|
||||
|
||||
**Additive only.** New algorithms (`BayesianOpt`, `Tpe`,
|
||||
`OnePlusOneEs`, `IpopCmaEs`, `SeparableNes`, `NelderMead`,
|
||||
`Hyperband`), new operators (`LevyMutation`, `ClampToBounds`,
|
||||
`ProjectToSimplex`), new traits (`PartialProblem`, `Repair<D>`).
|
||||
|
||||
`CmaEsConfig` gained an `initial_mean: Option<Vec<f64>>` field;
|
||||
existing call sites need a `.. CmaEsConfig { initial_mean: None,
|
||||
.. }` update.
|
||||
|
||||
## To 0.2
|
||||
|
||||
### From 0.1.x
|
||||
|
||||
**Additive.** New algorithms across the catalog (HillClimber, SA,
|
||||
GA, PSO, CMA-ES, TabuSearch, AntColonyTsp, Umda, TLBO, MOPSO, IBEA,
|
||||
SMS-EMOA, HypE, RVEA, PESA-II, ε-MOEA, AGE-MOEA, GrEA, KnEA), new
|
||||
operators (`SimulatedBinaryCrossover`, `PolynomialMutation`,
|
||||
`CompositeVariation`, `BoundedGaussianMutation`), and the
|
||||
`hypervolume_nd` metric.
|
||||
|
||||
`Optimizer<P>` impls now require `P: Sync` and `P::Decision: Send`
|
||||
(this enables the `parallel` feature without changing the public
|
||||
trait surface). Any normal `Problem` you've written satisfies these
|
||||
bounds automatically.
|
||||
@@ -0,0 +1,96 @@
|
||||
# Stability and SemVer
|
||||
|
||||
heuropt is pre-1.0. The public API may change between minor versions.
|
||||
This page sets explicit expectations.
|
||||
|
||||
## What "public API" means in heuropt
|
||||
|
||||
The crate's public surface is everything re-exported from
|
||||
[`heuropt::prelude`] plus the items reachable from `heuropt::core`,
|
||||
`heuropt::traits`, `heuropt::operators`, `heuropt::algorithms`,
|
||||
`heuropt::pareto`, `heuropt::metrics`, and `heuropt::selection`.
|
||||
|
||||
Items in `heuropt::internal` (e.g. the Cholesky / eigendecomposition
|
||||
helpers) are **not** public API. They may change between any two
|
||||
versions — use them at your own risk.
|
||||
|
||||
## SemVer in heuropt 0.x
|
||||
|
||||
While we are pre-1.0:
|
||||
|
||||
- **Minor bumps (`0.5 → 0.6`) may break the public API.** The
|
||||
CHANGELOG calls out everything that changed, and a **migration
|
||||
guide** in this book documents the move.
|
||||
- **Patch bumps (`0.5.0 → 0.5.1`) only contain bug fixes,
|
||||
performance improvements, and additive non-breaking features.**
|
||||
No deprecations, no removals.
|
||||
|
||||
## What's actually likely to change before 1.0
|
||||
|
||||
In rough order of likelihood:
|
||||
|
||||
1. **`Optimizer<P>` may grow new optional methods** for callbacks,
|
||||
stop conditions, and save/resume support. These will land as
|
||||
methods with default implementations so existing trait impls
|
||||
keep compiling, but the trait shape will be different.
|
||||
2. **Algorithm config structs may gain fields.** All current configs
|
||||
are public-field structs; adding a non-`Default` field is a
|
||||
breaking change. We may switch to builder patterns to avoid this
|
||||
class of break, or we may add `#[non_exhaustive]`.
|
||||
3. **The `Snapshot`, `Observer`, and `Checkpoint` types** (planned
|
||||
for a future release) will land as new public surfaces.
|
||||
4. **Some operators may move between `operators` and `pareto`** as
|
||||
the boundary between "things that produce candidates" and "Pareto
|
||||
utilities" gets clearer.
|
||||
|
||||
What is **not** likely to change:
|
||||
|
||||
- The `Problem` trait shape.
|
||||
- The `Variation` / `Initializer` / `Repair` traits.
|
||||
- The `Evaluation` / `Candidate` / `Population` / `OptimizationResult`
|
||||
data types.
|
||||
- The seeded determinism property.
|
||||
|
||||
## What "bit-identical" means for stability
|
||||
|
||||
heuropt promises that a given algorithm + seed + config produces the
|
||||
same numeric output on the same minor version of heuropt.
|
||||
|
||||
Across minor versions, output may change if an algorithm's
|
||||
implementation changes (e.g. a perf rewrite that reorders
|
||||
floating-point operations, or a new feature that changes the
|
||||
RNG-consumption pattern). The CHANGELOG calls this out explicitly
|
||||
when it happens. As of v0.5, the entire history of perf optimizations
|
||||
has been bit-identical against the v0.3.0 reference.
|
||||
|
||||
## MSRV (minimum supported Rust version)
|
||||
|
||||
heuropt's MSRV is **1.85** as of v0.5. This is tested in CI against
|
||||
every PR.
|
||||
|
||||
MSRV bumps are treated as patch-bump-eligible (they don't break the
|
||||
public API). When the MSRV is bumped, the CHANGELOG entry for that
|
||||
release will note the new MSRV.
|
||||
|
||||
## Feature-flag stability
|
||||
|
||||
The current optional features:
|
||||
|
||||
- `serde` — adds `Serialize` / `Deserialize` derives on the core data
|
||||
types.
|
||||
- `parallel` — rayon-backed parallel population evaluation.
|
||||
|
||||
Features added in 0.x can be renamed or removed in any minor bump
|
||||
that documents the change. Removing a feature is treated like a
|
||||
breaking API change.
|
||||
|
||||
## How to track changes
|
||||
|
||||
- **CHANGELOG.md** — the canonical record of changes per release.
|
||||
- **Migration guides** — per-release, in this book at
|
||||
[migration](./migration.md).
|
||||
- **GitHub releases** — each tag has release notes.
|
||||
- **Watch the repo** — https://github.com/swaits/heuropt — to be
|
||||
notified of new releases.
|
||||
|
||||
[`heuropt::prelude`]: https://docs.rs/heuropt/latest/heuropt/prelude/index.html
|
||||
@@ -0,0 +1,125 @@
|
||||
//! Constrained multi-objective optimization (BNH problem) plus a
|
||||
//! demo of the observer / stop-condition API.
|
||||
//!
|
||||
//! BNH (Binh & Korn 1996) is a 2-variable / 2-objective / 2-constraint
|
||||
//! multi-objective problem:
|
||||
//!
|
||||
//! ```text
|
||||
//! minimize f1 = 4·x1² + 4·x2²
|
||||
//! f2 = (x1 − 5)² + (x2 − 5)²
|
||||
//! subject to
|
||||
//! g1: (x1 − 5)² + x2² ≤ 25
|
||||
//! g2: (x1 − 8)² + (x2 + 3)² ≥ 7.7
|
||||
//! 0 ≤ x1 ≤ 5, 0 ≤ x2 ≤ 3
|
||||
//! ```
|
||||
//!
|
||||
//! Demonstrates:
|
||||
//! - Constraint handling via `Evaluation::constrained` (heuropt's
|
||||
//! default tournament/Pareto comparators prefer feasibles).
|
||||
//! - The Observer API: a `Stagnation` observer that halts the run
|
||||
//! once the front stops improving, plus a `Periodic` observer that
|
||||
//! prints progress every 25 generations.
|
||||
//! - Composing observers with `.or()`.
|
||||
//!
|
||||
//! Run with: `cargo run --release --example constrained`
|
||||
|
||||
use heuropt::prelude::*;
|
||||
|
||||
struct Bnh;
|
||||
|
||||
impl Problem for Bnh {
|
||||
type Decision = Vec<f64>;
|
||||
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
}
|
||||
|
||||
fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
let f1 = 4.0 * x[0] * x[0] + 4.0 * x[1] * x[1];
|
||||
let f2 = (x[0] - 5.0).powi(2) + (x[1] - 5.0).powi(2);
|
||||
|
||||
// g1: (x1 − 5)² + x2² ≤ 25 → violation = max(0, lhs − 25)
|
||||
let g1 = ((x[0] - 5.0).powi(2) + x[1].powi(2) - 25.0).max(0.0);
|
||||
// g2: (x1 − 8)² + (x2 + 3)² ≥ 7.7 → violation = max(0, 7.7 − lhs)
|
||||
let g2 = (7.7 - ((x[0] - 8.0).powi(2) + (x[1] + 3.0).powi(2))).max(0.0);
|
||||
|
||||
let total_violation = g1 + g2;
|
||||
Evaluation::constrained(vec![f1, f2], total_violation)
|
||||
}
|
||||
}
|
||||
|
||||
fn main() {
|
||||
let bounds = vec![(0.0_f64, 5.0_f64), (0.0_f64, 3.0_f64)];
|
||||
|
||||
// Compose stop conditions: halt after 5 s OR (via .or()) print
|
||||
// periodic progress every 25 generations. The Periodic observer
|
||||
// never breaks; it only logs.
|
||||
let stop = MaxTime::new(std::time::Duration::from_secs(5));
|
||||
let progress = Periodic::new(25, |snap: &Snapshot<'_, Vec<f64>>| {
|
||||
let feasible_in_pop = snap
|
||||
.population
|
||||
.iter()
|
||||
.filter(|c| c.evaluation.is_feasible())
|
||||
.count();
|
||||
let front_size = snap.pareto_front.map(|f| f.len()).unwrap_or(0);
|
||||
println!(
|
||||
"gen {:>4} evaluations = {:>6} feasible/pop = {}/{} front = {}",
|
||||
snap.iteration,
|
||||
snap.evaluations,
|
||||
feasible_in_pop,
|
||||
snap.population.len(),
|
||||
front_size,
|
||||
);
|
||||
});
|
||||
let mut observer = <_ as Observer<Vec<f64>>>::or(stop, progress);
|
||||
|
||||
let mut opt = Nsga2::new(
|
||||
Nsga2Config {
|
||||
population_size: 100,
|
||||
generations: 250,
|
||||
seed: 42,
|
||||
},
|
||||
RealBounds::new(bounds.clone()),
|
||||
CompositeVariation {
|
||||
crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
mutation: PolynomialMutation::new(bounds, 20.0, 1.0 / 2.0),
|
||||
},
|
||||
);
|
||||
let result = opt.run_with(&Bnh, &mut observer);
|
||||
|
||||
let total_feasible = result
|
||||
.population
|
||||
.iter()
|
||||
.filter(|c| c.evaluation.is_feasible())
|
||||
.count();
|
||||
|
||||
println!();
|
||||
println!("Final state after {} generations:", result.generations);
|
||||
println!(" total evaluations: {}", result.evaluations);
|
||||
println!(
|
||||
" feasible / total pop: {} / {}",
|
||||
total_feasible,
|
||||
result.population.len()
|
||||
);
|
||||
println!(" pareto front size: {}", result.pareto_front.len());
|
||||
println!();
|
||||
println!("Sample of the front (f1, f2):");
|
||||
let mut sorted = result.pareto_front.clone();
|
||||
sorted.sort_by(|a, b| {
|
||||
a.evaluation.objectives[0]
|
||||
.partial_cmp(&b.evaluation.objectives[0])
|
||||
.unwrap_or(std::cmp::Ordering::Equal)
|
||||
});
|
||||
let n = sorted.len();
|
||||
if n > 0 {
|
||||
for k in (0..n).step_by((n / 5).max(1)) {
|
||||
let c = &sorted[k];
|
||||
println!(
|
||||
" f1 = {:>7.3}, f2 = {:>7.3}, violation = {:.3}",
|
||||
c.evaluation.objectives[0],
|
||||
c.evaluation.objectives[1],
|
||||
c.evaluation.constraint_violation,
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,124 @@
|
||||
//! Tune a synthetic ML model's hyperparameters with Bayesian Optimization
|
||||
//! and (separately) Tree-structured Parzen Estimator.
|
||||
//!
|
||||
//! The "model" here is a deterministic function over `(learning_rate,
|
||||
//! weight_decay, depth)` that mimics the shape of a real validation-loss
|
||||
//! surface — a noisy minimum near sensible hyperparameters with sharp
|
||||
//! penalties as you stray. It's compute-cheap so the example runs in
|
||||
//! seconds, but the *workflow* is exactly what you'd use on a real
|
||||
//! 30-second-per-eval model.
|
||||
//!
|
||||
//! Demonstrates:
|
||||
//! - Sample-efficient optimization: 60 evaluations total, not 60,000.
|
||||
//! - Comparing BO vs TPE on the same problem with the same budget.
|
||||
//! - Decoding decision vectors with mixed scales (log-uniform learning
|
||||
//! rate, integer-valued depth) using transforms inside `evaluate`.
|
||||
//!
|
||||
//! Run with: `cargo run --release --example hyperparam_tuning`
|
||||
|
||||
use heuropt::prelude::*;
|
||||
|
||||
/// A pretend deep-learning model whose validation loss is a
|
||||
/// reproducible analytic function of three hyperparameters.
|
||||
struct ModelTuning;
|
||||
|
||||
impl Problem for ModelTuning {
|
||||
type Decision = Vec<f64>;
|
||||
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("val_loss")])
|
||||
}
|
||||
|
||||
fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
// The decision vector is in [0, 1] per dim; we decode each axis
|
||||
// into the "real" hyperparameter space.
|
||||
let lr = log_uniform(x[0], 1e-5, 1e-1); // learning rate
|
||||
let wd = log_uniform(x[1], 1e-6, 1e-2); // weight decay
|
||||
let depth = scale_to_int(x[2], 2, 12); // num layers
|
||||
|
||||
// Synthetic validation loss surface:
|
||||
// * minimum at lr ≈ 1e-3, wd ≈ 1e-4, depth = 6
|
||||
// * log-quadratic in lr / wd (typical hyperparameter shape)
|
||||
// * mild penalty for depth far from 6
|
||||
// * tiny deterministic "noise" so flat regions don't all tie
|
||||
let lr_term = (lr.log10() - (-3.0)).powi(2);
|
||||
let wd_term = (wd.log10() - (-4.0)).powi(2);
|
||||
let depth_term = 0.05 * ((depth as f64 - 6.0).abs());
|
||||
let noise = 0.02 * ((10.0 * x[0] + 17.0 * x[1] + 23.0 * x[2]).sin());
|
||||
|
||||
let val_loss = 0.05 + 0.3 * lr_term + 0.2 * wd_term + depth_term + noise;
|
||||
Evaluation::new(vec![val_loss])
|
||||
}
|
||||
}
|
||||
|
||||
fn log_uniform(unit: f64, lo: f64, hi: f64) -> f64 {
|
||||
let log_lo = lo.ln();
|
||||
let log_hi = hi.ln();
|
||||
(log_lo + unit * (log_hi - log_lo)).exp()
|
||||
}
|
||||
|
||||
fn scale_to_int(unit: f64, lo: i32, hi: i32) -> i32 {
|
||||
let span = (hi - lo + 1) as f64;
|
||||
let i = (unit * span).floor() as i32;
|
||||
(lo + i).min(hi)
|
||||
}
|
||||
|
||||
fn run_bo(seed: u64) -> OptimizationResult<Vec<f64>> {
|
||||
let mut opt = BayesianOpt::new(
|
||||
BayesianOptConfig {
|
||||
initial_samples: 10,
|
||||
iterations: 50, // 60 total evals
|
||||
length_scales: None,
|
||||
signal_variance: 1.0,
|
||||
noise_variance: 1e-6,
|
||||
acquisition_samples: 200,
|
||||
seed,
|
||||
},
|
||||
RealBounds::new(vec![(0.0, 1.0); 3]),
|
||||
);
|
||||
opt.run(&ModelTuning)
|
||||
}
|
||||
|
||||
fn run_tpe(seed: u64) -> OptimizationResult<Vec<f64>> {
|
||||
let mut opt = Tpe::new(
|
||||
TpeConfig {
|
||||
initial_samples: 10,
|
||||
iterations: 50, // 60 total evals
|
||||
good_fraction: 0.25,
|
||||
candidate_samples: 64,
|
||||
bandwidth_factor: 1.0,
|
||||
seed,
|
||||
},
|
||||
RealBounds::new(vec![(0.0, 1.0); 3]),
|
||||
);
|
||||
opt.run(&ModelTuning)
|
||||
}
|
||||
|
||||
fn report(name: &str, r: &OptimizationResult<Vec<f64>>) {
|
||||
let best = r.best.as_ref().expect("at least one feasible candidate");
|
||||
let lr = log_uniform(best.decision[0], 1e-5, 1e-1);
|
||||
let wd = log_uniform(best.decision[1], 1e-6, 1e-2);
|
||||
let depth = scale_to_int(best.decision[2], 2, 12);
|
||||
println!(
|
||||
"{:<8} val_loss = {:>7.4} | lr = {:>10.2e} wd = {:>10.2e} depth = {} | evals = {}",
|
||||
name, best.evaluation.objectives[0], lr, wd, depth, r.evaluations,
|
||||
);
|
||||
}
|
||||
|
||||
fn main() {
|
||||
println!("Tuning ModelTuning (synthetic 3-D loss surface)");
|
||||
println!("Optimum: lr ≈ 1e-3, wd ≈ 1e-4, depth = 6, val_loss ≈ 0.03");
|
||||
println!();
|
||||
println!(
|
||||
"{:<8} {:<26} {:<24} {:<24}",
|
||||
"alg", "best", "(decoded hyperparams)", "(eval budget)"
|
||||
);
|
||||
for seed in 0..5 {
|
||||
println!();
|
||||
println!("seed {}:", seed);
|
||||
let bo = run_bo(seed);
|
||||
let tpe = run_tpe(seed);
|
||||
report("BO", &bo);
|
||||
report("TPE", &tpe);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,210 @@
|
||||
//! Multi-objective portfolio optimization with a budget constraint.
|
||||
//!
|
||||
//! Real-world flavor: pick a portfolio over five synthetic assets that
|
||||
//! trades off **return** (maximize) against **risk** (minimize). Weights
|
||||
//! must be non-negative and sum to 1.0 (the standard probability-simplex
|
||||
//! budget constraint).
|
||||
//!
|
||||
//! Demonstrates:
|
||||
//! - Multi-objective formulation with a maximize axis (return) and a
|
||||
//! minimize axis (variance-based risk).
|
||||
//! - The `ProjectToSimplex` repair operator wired into a `Repair`-aware
|
||||
//! variation pipeline so every offspring respects the budget.
|
||||
//! - NSGA-II producing a Pareto front of trade-offs.
|
||||
//! - Picking one answer off the front via a-posteriori weighting (see
|
||||
//! `docs/book/src/cookbook/pick-one.md`).
|
||||
//!
|
||||
//! Run with: `cargo run --release --example portfolio`
|
||||
|
||||
use heuropt::prelude::*;
|
||||
|
||||
/// Five-asset toy market. Means and a covariance matrix you'd estimate
|
||||
/// from real returns; here they're synthetic but realistic-shape.
|
||||
struct Portfolio {
|
||||
/// Expected per-period returns (one per asset).
|
||||
expected_returns: [f64; 5],
|
||||
/// Symmetric 5×5 covariance matrix.
|
||||
covariance: [[f64; 5]; 5],
|
||||
}
|
||||
|
||||
impl Problem for Portfolio {
|
||||
type Decision = Vec<f64>;
|
||||
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![
|
||||
Objective::maximize("return"),
|
||||
Objective::minimize("risk"),
|
||||
])
|
||||
}
|
||||
|
||||
fn evaluate(&self, weights: &Vec<f64>) -> Evaluation {
|
||||
// Expected return: w · μ
|
||||
let r: f64 = weights
|
||||
.iter()
|
||||
.zip(self.expected_returns.iter())
|
||||
.map(|(w, m)| w * m)
|
||||
.sum();
|
||||
|
||||
// Risk (portfolio variance): w · Σ · w
|
||||
let mut risk = 0.0;
|
||||
for i in 0..5 {
|
||||
for j in 0..5 {
|
||||
risk += weights[i] * self.covariance[i][j] * weights[j];
|
||||
}
|
||||
}
|
||||
|
||||
Evaluation::new(vec![r, risk])
|
||||
}
|
||||
}
|
||||
|
||||
/// Variation pipeline that respects the simplex constraint: SBX +
|
||||
/// PolyMut produce real-valued children, then `ProjectToSimplex` projects
|
||||
/// them back onto `{ w : w ≥ 0, Σw = 1 }`.
|
||||
struct SimplexVariation {
|
||||
crossover: SimulatedBinaryCrossover,
|
||||
mutation: PolynomialMutation,
|
||||
repair: ProjectToSimplex,
|
||||
}
|
||||
|
||||
impl Variation<Vec<f64>> for SimplexVariation {
|
||||
fn vary(&mut self, parents: &[Vec<f64>], rng: &mut Rng) -> Vec<Vec<f64>> {
|
||||
let crossed = self.crossover.vary(parents, rng);
|
||||
let mut out = Vec::with_capacity(crossed.len());
|
||||
for child in crossed {
|
||||
let mut mutated = self
|
||||
.mutation
|
||||
.vary(std::slice::from_ref(&child), rng)
|
||||
.pop()
|
||||
.expect("PolynomialMutation returned no child");
|
||||
self.repair.repair(&mut mutated);
|
||||
out.push(mutated);
|
||||
}
|
||||
out
|
||||
}
|
||||
}
|
||||
|
||||
/// `Initializer` that uniformly samples points on the simplex via the
|
||||
/// standard "log-and-normalize" trick. Every initial member is feasible
|
||||
/// by construction.
|
||||
struct SimplexInit {
|
||||
dim: usize,
|
||||
}
|
||||
|
||||
impl Initializer<Vec<f64>> for SimplexInit {
|
||||
fn initialize(&mut self, size: usize, rng: &mut Rng) -> Vec<Vec<f64>> {
|
||||
use rand::Rng as _;
|
||||
let mut out = Vec::with_capacity(size);
|
||||
for _ in 0..size {
|
||||
// Sample exponentials, normalize → uniform on simplex.
|
||||
let mut e: Vec<f64> = (0..self.dim)
|
||||
.map(|_| -(1.0_f64 - rng.random::<f64>()).ln())
|
||||
.collect();
|
||||
let s: f64 = e.iter().sum();
|
||||
for v in e.iter_mut() {
|
||||
*v /= s;
|
||||
}
|
||||
out.push(e);
|
||||
}
|
||||
out
|
||||
}
|
||||
}
|
||||
|
||||
fn main() {
|
||||
let problem = Portfolio {
|
||||
// Synthetic but plausible: 8% / 12% / 5% / 15% / 3% expected
|
||||
// returns. The two "stocks" (B, D) have higher expected return
|
||||
// and higher variance than the bonds / cash equivalents.
|
||||
expected_returns: [0.08, 0.12, 0.05, 0.15, 0.03],
|
||||
covariance: [
|
||||
[0.04, 0.02, 0.01, 0.03, 0.005],
|
||||
[0.02, 0.10, 0.01, 0.05, 0.005],
|
||||
[0.01, 0.01, 0.02, 0.01, 0.005],
|
||||
[0.03, 0.05, 0.01, 0.16, 0.005],
|
||||
[0.005, 0.005, 0.005, 0.005, 0.001],
|
||||
],
|
||||
};
|
||||
|
||||
let bounds = vec![(0.0_f64, 1.0_f64); 5];
|
||||
|
||||
let variation = SimplexVariation {
|
||||
crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 1.0),
|
||||
mutation: PolynomialMutation::new(bounds.clone(), 20.0, 1.0 / 5.0),
|
||||
repair: ProjectToSimplex::new(1.0),
|
||||
};
|
||||
|
||||
let mut opt = Nsga2::new(
|
||||
Nsga2Config {
|
||||
population_size: 100,
|
||||
generations: 200,
|
||||
seed: 42,
|
||||
},
|
||||
SimplexInit { dim: 5 },
|
||||
variation,
|
||||
);
|
||||
|
||||
let result = opt.run(&problem);
|
||||
|
||||
println!("Pareto front size: {}", result.pareto_front.len());
|
||||
println!("Total evaluations: {}", result.evaluations);
|
||||
|
||||
// Pick one: a-posteriori weighted decision favoring return slightly.
|
||||
// Lower score = preferred. We compare in oriented space (maximize
|
||||
// axis already flipped to negative by `as_minimization`).
|
||||
let space = problem.objectives();
|
||||
let weights = [1.0, 1.5]; // weight risk a bit more than -return
|
||||
let chosen = result
|
||||
.pareto_front
|
||||
.iter()
|
||||
.min_by(|a, b| {
|
||||
let ax: f64 = space
|
||||
.as_minimization(&a.evaluation.objectives)
|
||||
.iter()
|
||||
.zip(&weights)
|
||||
.map(|(v, w)| v * w)
|
||||
.sum();
|
||||
let bx: f64 = space
|
||||
.as_minimization(&b.evaluation.objectives)
|
||||
.iter()
|
||||
.zip(&weights)
|
||||
.map(|(v, w)| v * w)
|
||||
.sum();
|
||||
ax.partial_cmp(&bx).unwrap_or(std::cmp::Ordering::Equal)
|
||||
})
|
||||
.expect("non-empty front");
|
||||
|
||||
println!();
|
||||
println!(
|
||||
"Picked portfolio: weights = [{:.3}, {:.3}, {:.3}, {:.3}, {:.3}]",
|
||||
chosen.decision[0],
|
||||
chosen.decision[1],
|
||||
chosen.decision[2],
|
||||
chosen.decision[3],
|
||||
chosen.decision[4],
|
||||
);
|
||||
println!(
|
||||
" expected return: {:>6.4}",
|
||||
chosen.evaluation.objectives[0]
|
||||
);
|
||||
println!(
|
||||
" risk (variance): {:>6.4}",
|
||||
chosen.evaluation.objectives[1]
|
||||
);
|
||||
|
||||
// Print 5 representative points across the front.
|
||||
println!();
|
||||
println!("Sample of the front (return, risk):");
|
||||
let mut sorted = result.pareto_front.clone();
|
||||
sorted.sort_by(|a, b| {
|
||||
a.evaluation.objectives[0]
|
||||
.partial_cmp(&b.evaluation.objectives[0])
|
||||
.unwrap_or(std::cmp::Ordering::Equal)
|
||||
});
|
||||
let n = sorted.len();
|
||||
for k in (0..n).step_by((n / 5).max(1)) {
|
||||
let c = &sorted[k];
|
||||
println!(
|
||||
" return = {:.4}, risk = {:.4}",
|
||||
c.evaluation.objectives[0], c.evaluation.objectives[1],
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,132 @@
|
||||
//! Single-machine job-shop scheduling: minimize total weighted
|
||||
//! completion time given per-job processing times and due-date weights.
|
||||
//!
|
||||
//! The decision is a permutation `Vec<usize>` — the order in which
|
||||
//! jobs are processed. We use `SimulatedAnnealing` paired with
|
||||
//! `SwapMutation` (the standard generic-permutation pair).
|
||||
//!
|
||||
//! Demonstrates:
|
||||
//! - Permutation decisions (`Vec<usize>`).
|
||||
//! - Simulated annealing with a custom `Initializer` that produces a
|
||||
//! randomly shuffled identity permutation.
|
||||
//! - `SwapMutation` preserving the permutation invariant for free.
|
||||
//!
|
||||
//! Run with: `cargo run --release --example scheduling`
|
||||
|
||||
use heuropt::prelude::*;
|
||||
|
||||
/// Single-machine weighted-completion-time problem (1 || Σwᵢ Cᵢ).
|
||||
struct Scheduling {
|
||||
/// Processing time for each job.
|
||||
process_times: Vec<f64>,
|
||||
/// Importance weight for each job. Higher weight = more
|
||||
/// punishing if the job finishes late.
|
||||
weights: Vec<f64>,
|
||||
}
|
||||
|
||||
impl Problem for Scheduling {
|
||||
type Decision = Vec<usize>;
|
||||
|
||||
fn objectives(&self) -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("total_wct")])
|
||||
}
|
||||
|
||||
fn evaluate(&self, schedule: &Vec<usize>) -> Evaluation {
|
||||
// Compute each job's completion time as the running sum of
|
||||
// processing times in the chosen order.
|
||||
let mut clock = 0.0_f64;
|
||||
let mut total_wct = 0.0_f64;
|
||||
for &job in schedule {
|
||||
clock += self.process_times[job];
|
||||
total_wct += self.weights[job] * clock;
|
||||
}
|
||||
Evaluation::new(vec![total_wct])
|
||||
}
|
||||
}
|
||||
|
||||
/// Initializer that produces a single randomly-shuffled permutation
|
||||
/// `[0, 1, …, n-1]`. Simulated annealing only needs one initial decision.
|
||||
struct ShuffledPerm {
|
||||
n: usize,
|
||||
}
|
||||
|
||||
impl Initializer<Vec<usize>> for ShuffledPerm {
|
||||
fn initialize(&mut self, _size: usize, rng: &mut Rng) -> Vec<Vec<usize>> {
|
||||
use rand::seq::SliceRandom;
|
||||
let mut perm: Vec<usize> = (0..self.n).collect();
|
||||
perm.shuffle(rng);
|
||||
vec![perm]
|
||||
}
|
||||
}
|
||||
|
||||
fn main() {
|
||||
// 12 jobs. The optimal policy is the Smith's-rule order: sort by
|
||||
// p_i / w_i ascending (shortest weighted processing time first).
|
||||
// We can compute that directly to compare against the search result.
|
||||
let jobs = [
|
||||
(3.0_f64, 2.0_f64),
|
||||
(5.0, 1.0),
|
||||
(2.0, 4.0),
|
||||
(8.0, 3.0),
|
||||
(4.0, 5.0),
|
||||
(1.0, 2.0),
|
||||
(7.0, 6.0),
|
||||
(6.0, 1.0),
|
||||
(3.0, 3.0),
|
||||
(5.0, 4.0),
|
||||
(2.0, 2.0),
|
||||
(4.0, 1.0),
|
||||
];
|
||||
let process_times: Vec<f64> = jobs.iter().map(|j| j.0).collect();
|
||||
let weights: Vec<f64> = jobs.iter().map(|j| j.1).collect();
|
||||
let n = jobs.len();
|
||||
|
||||
let problem = Scheduling {
|
||||
process_times: process_times.clone(),
|
||||
weights: weights.clone(),
|
||||
};
|
||||
|
||||
// Smith's rule oracle: sort jobs by p / w ascending.
|
||||
let mut smith_order: Vec<usize> = (0..n).collect();
|
||||
smith_order.sort_by(|&a, &b| {
|
||||
let ra = process_times[a] / weights[a];
|
||||
let rb = process_times[b] / weights[b];
|
||||
ra.partial_cmp(&rb).unwrap_or(std::cmp::Ordering::Equal)
|
||||
});
|
||||
let smith_score = problem.evaluate(&smith_order).objectives[0];
|
||||
|
||||
// Search via simulated annealing with swap mutation.
|
||||
let mut opt = SimulatedAnnealing::new(
|
||||
SimulatedAnnealingConfig {
|
||||
iterations: 5_000,
|
||||
initial_temperature: 50.0,
|
||||
final_temperature: 1e-3,
|
||||
seed: 42,
|
||||
},
|
||||
ShuffledPerm { n },
|
||||
SwapMutation,
|
||||
);
|
||||
let result = opt.run(&problem);
|
||||
|
||||
let best = result.best.unwrap();
|
||||
println!("Single-machine weighted completion time, {} jobs", n);
|
||||
println!();
|
||||
println!(
|
||||
"Smith's-rule oracle: {:>8.2} order = {:?}",
|
||||
smith_score, smith_order
|
||||
);
|
||||
println!(
|
||||
"Simulated annealing best: {:>8.2} order = {:?}",
|
||||
best.evaluation.objectives[0], best.decision,
|
||||
);
|
||||
println!(
|
||||
"Random initial schedule: {:>8.2} order = {:?}",
|
||||
problem.evaluate(&(0..n).collect()).objectives[0],
|
||||
(0..n).collect::<Vec<usize>>(),
|
||||
);
|
||||
println!();
|
||||
println!(
|
||||
"SA reached optimum (Smith): {}",
|
||||
(best.evaluation.objectives[0] - smith_score).abs() < 1e-9
|
||||
);
|
||||
}
|
||||
@@ -40,6 +40,35 @@ impl Default for AgeMoeaConfig {
|
||||
/// score survivors by a combination of proximity (distance to the
|
||||
/// translated origin in the L_p frame) and diversity (distance to the
|
||||
/// nearest survivor in the same frame).
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = AgeMoea::new(
|
||||
/// AgeMoeaConfig { population_size: 30, generations: 20, seed: 42 },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct AgeMoea<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -57,6 +57,53 @@ impl Default for AntColonyTspConfig {
|
||||
/// Each ant builds a tour by repeatedly choosing the next node with
|
||||
/// probability `∝ τ_ij^α · η_ij^β` over the unvisited cities, where
|
||||
/// `η_ij = 1 / distance_ij` is the heuristic desirability.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Tsp { distances: Vec<Vec<f64>> }
|
||||
/// impl Problem for Tsp {
|
||||
/// type Decision = Vec<usize>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("length")])
|
||||
/// }
|
||||
/// fn evaluate(&self, tour: &Vec<usize>) -> Evaluation {
|
||||
/// let mut len = 0.0;
|
||||
/// for w in tour.windows(2) { len += self.distances[w[0]][w[1]]; }
|
||||
/// len += self.distances[*tour.last().unwrap()][tour[0]];
|
||||
/// Evaluation::new(vec![len])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// // 5 cities laid out in a small square + center. The optimal tour
|
||||
/// // is the perimeter; the diagonal is suboptimal.
|
||||
/// let cities = [(0.0_f64, 0.0), (3.0, 0.0), (3.0, 3.0), (0.0, 3.0), (1.5, 1.5)];
|
||||
/// let n = cities.len();
|
||||
/// let mut d = vec![vec![0.0; n]; n];
|
||||
/// for i in 0..n {
|
||||
/// for j in 0..n {
|
||||
/// let dx = cities[i].0 - cities[j].0;
|
||||
/// let dy = cities[i].1 - cities[j].1;
|
||||
/// d[i][j] = (dx * dx + dy * dy).sqrt();
|
||||
/// }
|
||||
/// }
|
||||
/// let problem = Tsp { distances: d.clone() };
|
||||
///
|
||||
/// let mut opt = AntColonyTsp::new(AntColonyTspConfig {
|
||||
/// ants: 10,
|
||||
/// generations: 50,
|
||||
/// alpha: 1.0,
|
||||
/// beta: 5.0,
|
||||
/// evaporation: 0.5,
|
||||
/// deposit: 1.0,
|
||||
/// initial_pheromone: 0.1,
|
||||
/// seed: 42,
|
||||
/// }, d);
|
||||
/// let r = opt.run(&problem);
|
||||
/// assert!(r.best.is_some());
|
||||
/// ```
|
||||
pub struct AntColonyTsp {
|
||||
/// Algorithm configuration.
|
||||
pub config: AntColonyTspConfig,
|
||||
|
||||
@@ -61,6 +61,40 @@ impl Default for BayesianOptConfig {
|
||||
/// evaluation budgets (50–500). The GP kernel is anisotropic RBF; the
|
||||
/// acquisition function is EI; both are optimized by best-of-N random
|
||||
/// sampling each step (simple, predictable cost).
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = BayesianOpt::new(
|
||||
/// BayesianOptConfig {
|
||||
/// initial_samples: 10,
|
||||
/// iterations: 30,
|
||||
/// length_scales: None, // default per-axis length scales
|
||||
/// signal_variance: 1.0,
|
||||
/// noise_variance: 1e-6,
|
||||
/// acquisition_samples: 200,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(vec![(-3.0, 3.0); 3]),
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// // 10 random + 30 BO steps = 40 total evaluations.
|
||||
/// assert_eq!(r.evaluations, 40);
|
||||
/// assert!(r.best.is_some());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct BayesianOpt {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -59,6 +59,38 @@ impl Default for CmaEsConfig {
|
||||
/// `Vec<f64>` decisions only. Bounds come from the embedded `RealBounds`
|
||||
/// field; both the initial mean and every offspring are clamped per
|
||||
/// dimension.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = CmaEs::new(
|
||||
/// CmaEsConfig {
|
||||
/// population_size: 12,
|
||||
/// generations: 100,
|
||||
/// initial_sigma: 1.0,
|
||||
/// eigen_decomposition_period: 1,
|
||||
/// initial_mean: None,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0); 5]),
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// // CMA-ES converges aggressively on Sphere.
|
||||
/// assert!(r.best.unwrap().evaluation.objectives[0] < 1e-3);
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CmaEs {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -3,7 +3,6 @@
|
||||
use rand::Rng as _;
|
||||
|
||||
use crate::algorithms::parallel_eval::evaluate_batch;
|
||||
use crate::core::candidate::Candidate;
|
||||
use crate::core::objective::Direction;
|
||||
use crate::core::population::Population;
|
||||
use crate::core::problem::Problem;
|
||||
@@ -44,6 +43,37 @@ impl Default for DifferentialEvolutionConfig {
|
||||
///
|
||||
/// `Vec<f64>` decisions only; single-objective problems only. Bounds come from
|
||||
/// the embedded `RealBounds`, and mutant vectors are clamped to those bounds.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = DifferentialEvolution::new(
|
||||
/// DifferentialEvolutionConfig {
|
||||
/// population_size: 20,
|
||||
/// generations: 50,
|
||||
/// differential_weight: 0.5,
|
||||
/// crossover_probability: 0.9,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0); 5]),
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// // DE crushes Sphere; expect very small objective.
|
||||
/// assert!(r.best.unwrap().evaluation.objectives[0] < 1e-3);
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct DifferentialEvolution {
|
||||
/// Algorithm configuration.
|
||||
@@ -64,6 +94,16 @@ where
|
||||
P: Problem<Decision = Vec<f64>> + Sync,
|
||||
{
|
||||
fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision> {
|
||||
self.run_with(problem, &mut ())
|
||||
}
|
||||
|
||||
fn run_with<O>(&mut self, problem: &P, observer: &mut O) -> OptimizationResult<P::Decision>
|
||||
where
|
||||
O: crate::observer::Observer<P::Decision>,
|
||||
{
|
||||
use crate::observer::Snapshot;
|
||||
use std::ops::ControlFlow;
|
||||
|
||||
assert!(
|
||||
self.config.population_size >= 4,
|
||||
"DifferentialEvolution requires population_size >= 4 (DE/rand/1 needs three distinct donors plus the target)",
|
||||
@@ -79,6 +119,7 @@ where
|
||||
"DifferentialEvolution only supports single-objective problems",
|
||||
);
|
||||
let direction = objectives.objectives[0].direction;
|
||||
let started = std::time::Instant::now();
|
||||
|
||||
let dim = self.bounds.bounds.len();
|
||||
let n = self.config.population_size;
|
||||
@@ -91,12 +132,39 @@ where
|
||||
};
|
||||
let initial_pop = evaluate_batch(problem, decisions.clone());
|
||||
let mut evaluations = initial_pop.len();
|
||||
let mut evals: Vec<f64> = initial_pop
|
||||
let mut current_pop = initial_pop;
|
||||
let mut evals: Vec<f64> = current_pop
|
||||
.iter()
|
||||
.map(|c| c.evaluation.objectives[0])
|
||||
.collect();
|
||||
let mut completed_generations: usize = 0;
|
||||
|
||||
for _gen in 0..self.config.generations {
|
||||
// Initial snapshot.
|
||||
{
|
||||
let best = best_candidate(¤t_pop, &objectives);
|
||||
let snap = Snapshot {
|
||||
iteration: 0,
|
||||
evaluations,
|
||||
elapsed: started.elapsed(),
|
||||
population: ¤t_pop,
|
||||
pareto_front: None,
|
||||
best: best.as_ref(),
|
||||
objectives: &objectives,
|
||||
};
|
||||
if let ControlFlow::Break(()) = observer.observe(&snap) {
|
||||
let front = pareto_front(¤t_pop, &objectives);
|
||||
let best = best_candidate(¤t_pop, &objectives);
|
||||
return OptimizationResult::new(
|
||||
Population::new(current_pop),
|
||||
front,
|
||||
best,
|
||||
evaluations,
|
||||
completed_generations,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
for generation in 1..=self.config.generations {
|
||||
// Phase 1 (serial): construct one trial per target. RNG state is
|
||||
// consumed in deterministic order so seeded runs reproduce
|
||||
// exactly regardless of the `parallel` feature.
|
||||
@@ -133,22 +201,38 @@ where
|
||||
Direction::Maximize => trial_obj >= target_obj,
|
||||
};
|
||||
if trial_better {
|
||||
decisions[i] = trial_cand.decision;
|
||||
decisions[i] = trial_cand.decision.clone();
|
||||
evals[i] = trial_obj;
|
||||
current_pop[i] = trial_cand;
|
||||
}
|
||||
}
|
||||
completed_generations = generation;
|
||||
|
||||
// Per-generation snapshot.
|
||||
let best = best_candidate(¤t_pop, &objectives);
|
||||
let snap = Snapshot {
|
||||
iteration: generation,
|
||||
evaluations,
|
||||
elapsed: started.elapsed(),
|
||||
population: ¤t_pop,
|
||||
pareto_front: None,
|
||||
best: best.as_ref(),
|
||||
objectives: &objectives,
|
||||
};
|
||||
if let ControlFlow::Break(()) = observer.observe(&snap) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
let final_pop: Vec<Candidate<Vec<f64>>> = evaluate_batch(problem, decisions);
|
||||
evaluations += final_pop.len();
|
||||
let front = pareto_front(&final_pop, &objectives);
|
||||
let best = best_candidate(&final_pop, &objectives);
|
||||
// Re-evaluate to make sure final population is consistent (current_pop is already current).
|
||||
let front = pareto_front(¤t_pop, &objectives);
|
||||
let best = best_candidate(¤t_pop, &objectives);
|
||||
OptimizationResult::new(
|
||||
Population::new(final_pop),
|
||||
Population::new(current_pop),
|
||||
front,
|
||||
best,
|
||||
evaluations,
|
||||
self.config.generations,
|
||||
completed_generations,
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -39,6 +39,45 @@ impl Default for EpsilonMoeaConfig {
|
||||
}
|
||||
|
||||
/// ε-dominance MOEA.
|
||||
///
|
||||
/// Steady-state EA with an ε-grid archive: every member that lands in
|
||||
/// the same ε-box as an existing one is replaced by the closer point
|
||||
/// to the box's grid corner. Auto-bounds the front size by the choice
|
||||
/// of `epsilon`.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = EpsilonMoea::new(
|
||||
/// EpsilonMoeaConfig {
|
||||
/// population_size: 20,
|
||||
/// evaluations: 1_000,
|
||||
/// epsilon: vec![0.1, 0.1],
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct EpsilonMoea<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -46,6 +46,41 @@ impl Default for GeneticAlgorithmConfig {
|
||||
/// produces offspring, those are evaluated, and the next population is
|
||||
/// the top `elitism` from the previous generation plus the best
|
||||
/// `population_size - elitism` offspring (by fitness).
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64); 3];
|
||||
/// let mut opt = GeneticAlgorithm::new(
|
||||
/// GeneticAlgorithmConfig {
|
||||
/// population_size: 30,
|
||||
/// generations: 50,
|
||||
/// tournament_size: 2,
|
||||
/// elitism: 2,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// assert!(r.best.is_some());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct GeneticAlgorithm<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -38,6 +38,40 @@ impl Default for GreaConfig {
|
||||
}
|
||||
|
||||
/// Grid-based Evolutionary Algorithm (GrEA).
|
||||
///
|
||||
/// Many-objective EA that uses three grid-based metrics — grid rank,
|
||||
/// grid crowding distance, and grid coordinate point distance — to
|
||||
/// select survivors. Particularly strong on linear / simplex-shaped
|
||||
/// fronts (e.g. DTLZ1).
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = Grea::new(
|
||||
/// GreaConfig { population_size: 30, generations: 20, grid_divisions: 8, seed: 42 },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Grea<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -34,6 +34,31 @@ impl Default for HillClimberConfig {
|
||||
/// feasible beats infeasible, smaller violation wins among infeasibles.
|
||||
///
|
||||
/// Single-objective only.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = HillClimber::new(
|
||||
/// HillClimberConfig { iterations: 500, seed: 42 },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0); 3]),
|
||||
/// GaussianMutation { sigma: 0.3 },
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// assert!(r.best.is_some());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct HillClimber<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -48,6 +48,41 @@ impl Default for HypeConfig {
|
||||
|
||||
/// Hypervolume Estimation Algorithm: many-objective MOEA that selects via
|
||||
/// Monte Carlo–estimated hypervolume contributions.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = Hype::new(
|
||||
/// HypeConfig {
|
||||
/// population_size: 20,
|
||||
/// generations: 20,
|
||||
/// reference_point: vec![30.0, 30.0],
|
||||
/// mc_samples: 100,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Hype<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -52,6 +52,38 @@ impl Default for HyperbandConfig {
|
||||
/// low budget), later brackets favor exploitation (fewer configs run
|
||||
/// near the max budget). The single best result across all brackets
|
||||
/// is returned.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
/// use heuropt::core::partial_problem::PartialProblem;
|
||||
///
|
||||
/// struct Tuning;
|
||||
/// impl PartialProblem for Tuning {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("loss")])
|
||||
/// }
|
||||
/// fn evaluate_at_budget(&self, x: &Vec<f64>, budget: f64) -> Evaluation {
|
||||
/// // Pretend a model where more budget = lower loss.
|
||||
/// let loss = x[0].powi(2) + x[1].powi(2) + 1.0 / (budget + 1.0);
|
||||
/// Evaluation::new(vec![loss])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = Hyperband::new(
|
||||
/// HyperbandConfig {
|
||||
/// max_budget: 27.0,
|
||||
/// eta: 3.0,
|
||||
/// max_brackets: 4,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(vec![(-1.0, 1.0); 2]),
|
||||
/// );
|
||||
/// let r = opt.run(&Tuning);
|
||||
/// assert!(r.best.is_some());
|
||||
/// ```
|
||||
pub struct Hyperband<I, D>
|
||||
where
|
||||
D: Clone,
|
||||
|
||||
@@ -37,6 +37,40 @@ impl Default for IbeaConfig {
|
||||
}
|
||||
|
||||
/// IBEA (Indicator-Based EA) using the additive ε-indicator.
|
||||
///
|
||||
/// Selects survivors by their contribution to a quality indicator
|
||||
/// (additive ε) rather than by dominance + crowding. On the comparison
|
||||
/// harness it consistently produces the best convergence of the dominance-
|
||||
/// alternative methods on smooth and disconnected fronts alike.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = Ibea::new(
|
||||
/// IbeaConfig { population_size: 30, generations: 20, kappa: 0.05, seed: 42 },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Ibea<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -53,6 +53,42 @@ impl Default for IpopCmaEsConfig {
|
||||
}
|
||||
|
||||
/// IPOP-CMA-ES: CMA-ES with population-doubling restarts.
|
||||
///
|
||||
/// Specifically designed to fix vanilla CMA-ES's weakness on multimodal
|
||||
/// landscapes — each restart doubles the population and randomizes the
|
||||
/// initial mean to escape from local basins. On the comparison harness
|
||||
/// it drops vanilla CMA-ES's Rastrigin score from f = 2.35 to f = 0.13.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = IpopCmaEs::new(
|
||||
/// IpopCmaEsConfig {
|
||||
/// initial_population_size: 8,
|
||||
/// total_generations: 100,
|
||||
/// initial_sigma: 1.0,
|
||||
/// eigen_decomposition_period: 1,
|
||||
/// stall_generations: Some(20),
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0); 3]),
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// assert!(r.best.unwrap().evaluation.objectives[0] < 1.0);
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct IpopCmaEs {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -39,6 +39,35 @@ impl Default for KneaConfig {
|
||||
/// Survival selection ranks splitting-front members by perpendicular
|
||||
/// distance from the hyperplane connecting the front's extreme points.
|
||||
/// Larger distance ≈ stronger knee = preferred survivor.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = Knea::new(
|
||||
/// KneaConfig { population_size: 30, generations: 20, seed: 42 },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Knea<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -39,6 +39,45 @@ impl Default for MoeadConfig {
|
||||
}
|
||||
|
||||
/// MOEA/D optimizer using the Tchebycheff scalarizing function.
|
||||
///
|
||||
/// Decomposes the multi-objective problem into many single-objective
|
||||
/// scalarizations along Das–Dennis weight vectors and solves them
|
||||
/// in parallel with neighborhood-based mating. Very fast per generation;
|
||||
/// scales naturally to many objectives.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = Moead::new(
|
||||
/// MoeadConfig {
|
||||
/// generations: 30,
|
||||
/// reference_divisions: 19, // 20 weights for 2 objectives
|
||||
/// neighborhood_size: 5,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Moead<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -52,6 +52,38 @@ impl Default for MopsoConfig {
|
||||
/// `Vec<f64>` decisions only. Each particle maintains a personal best (the
|
||||
/// last position that was Pareto-non-dominated by any later position). The
|
||||
/// social leader is sampled uniformly from the external archive each step.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = Mopso::new(
|
||||
/// MopsoConfig {
|
||||
/// swarm_size: 30,
|
||||
/// generations: 50,
|
||||
/// archive_size: 30,
|
||||
/// inertia: 0.4,
|
||||
/// cognitive: 1.5,
|
||||
/// social: 1.5,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0)]),
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Mopso {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -48,6 +48,38 @@ impl Default for NelderMeadConfig {
|
||||
/// `Vec<f64>` decisions only. Single-objective only. Initial simplex is
|
||||
/// built around the midpoint of the configured bounds; every new vertex
|
||||
/// is clamped to those bounds.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = NelderMead::new(
|
||||
/// NelderMeadConfig {
|
||||
/// iterations: 200,
|
||||
/// reflection: 1.0,
|
||||
/// expansion: 2.0,
|
||||
/// contraction: 0.5,
|
||||
/// shrinkage: 0.5,
|
||||
/// initial_step: 1.0,
|
||||
/// },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0); 3]),
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// // Nelder-Mead reaches machine precision on Sphere.
|
||||
/// assert!(r.best.unwrap().evaluation.objectives[0] < 1e-10);
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct NelderMead {
|
||||
/// Algorithm configuration.
|
||||
|
||||
+106
-13
@@ -35,6 +35,43 @@ impl Default for Nsga2Config {
|
||||
}
|
||||
|
||||
/// NSGA-II optimizer (spec §12.3).
|
||||
///
|
||||
/// The canonical Pareto-based EA: combines non-dominated sorting with
|
||||
/// crowding-distance secondary ranking. A strong default for 2- or
|
||||
/// 3-objective problems.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![
|
||||
/// Objective::minimize("f1"),
|
||||
/// Objective::minimize("f2"),
|
||||
/// ])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = Nsga2::new(
|
||||
/// Nsga2Config { population_size: 30, generations: 20, seed: 42 },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert_eq!(r.population.len(), 30);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Nsga2<I, V> {
|
||||
/// Algorithm configuration.
|
||||
@@ -71,6 +108,16 @@ where
|
||||
V: Variation<P::Decision>,
|
||||
{
|
||||
fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision> {
|
||||
self.run_with(problem, &mut ())
|
||||
}
|
||||
|
||||
fn run_with<O>(&mut self, problem: &P, observer: &mut O) -> OptimizationResult<P::Decision>
|
||||
where
|
||||
O: crate::observer::Observer<P::Decision>,
|
||||
{
|
||||
use crate::observer::Snapshot;
|
||||
use std::ops::ControlFlow;
|
||||
|
||||
assert!(
|
||||
self.config.population_size > 0,
|
||||
"Nsga2 population_size must be greater than 0",
|
||||
@@ -78,6 +125,7 @@ where
|
||||
let n = self.config.population_size;
|
||||
let objectives = problem.objectives();
|
||||
let mut rng = rng_from_seed(self.config.seed);
|
||||
let started = std::time::Instant::now();
|
||||
|
||||
// Initial population.
|
||||
let initial_decisions = self.initializer.initialize(n, &mut rng);
|
||||
@@ -93,7 +141,27 @@ where
|
||||
// round of tournament selection has data to compare on.
|
||||
let mut annotated = annotate(population, &objectives);
|
||||
|
||||
for _ in 0..self.config.generations {
|
||||
// Observer: notify after the initial population.
|
||||
let mut completed_generations: usize = 0;
|
||||
let pop_view: Vec<Candidate<P::Decision>> =
|
||||
annotated.iter().map(|e| e.candidate.clone()).collect();
|
||||
let front_view = pareto_front(&pop_view, &objectives);
|
||||
let snap = Snapshot {
|
||||
iteration: 0,
|
||||
evaluations,
|
||||
elapsed: started.elapsed(),
|
||||
population: &pop_view,
|
||||
pareto_front: Some(&front_view),
|
||||
best: None,
|
||||
objectives: &objectives,
|
||||
};
|
||||
if let ControlFlow::Break(()) = observer.observe(&snap) {
|
||||
return finalize_nsga2(annotated, &objectives, evaluations, completed_generations);
|
||||
}
|
||||
drop(pop_view);
|
||||
drop(front_view);
|
||||
|
||||
for generation in 1..=self.config.generations {
|
||||
// --- Phase 1: serial parent selection + variation ---
|
||||
let mut offspring_decisions: Vec<P::Decision> = Vec::with_capacity(n);
|
||||
while offspring_decisions.len() < n {
|
||||
@@ -152,23 +220,48 @@ where
|
||||
}
|
||||
}
|
||||
annotated = annotate(next, &objectives);
|
||||
completed_generations = generation;
|
||||
|
||||
// Per-generation observation.
|
||||
let pop_view: Vec<Candidate<P::Decision>> =
|
||||
annotated.iter().map(|e| e.candidate.clone()).collect();
|
||||
let front_view = pareto_front(&pop_view, &objectives);
|
||||
let snap = Snapshot {
|
||||
iteration: generation,
|
||||
evaluations,
|
||||
elapsed: started.elapsed(),
|
||||
population: &pop_view,
|
||||
pareto_front: Some(&front_view),
|
||||
best: None,
|
||||
objectives: &objectives,
|
||||
};
|
||||
if let ControlFlow::Break(()) = observer.observe(&snap) {
|
||||
return finalize_nsga2(annotated, &objectives, evaluations, completed_generations);
|
||||
}
|
||||
}
|
||||
|
||||
// Return final state.
|
||||
let final_pop: Vec<Candidate<P::Decision>> =
|
||||
annotated.into_iter().map(|e| e.candidate).collect();
|
||||
let front = pareto_front(&final_pop, &objectives);
|
||||
let best = best_candidate(&final_pop, &objectives);
|
||||
OptimizationResult::new(
|
||||
Population::new(final_pop),
|
||||
front,
|
||||
best,
|
||||
evaluations,
|
||||
self.config.generations,
|
||||
)
|
||||
finalize_nsga2(annotated, &objectives, evaluations, self.config.generations)
|
||||
}
|
||||
}
|
||||
|
||||
fn finalize_nsga2<D: Clone>(
|
||||
annotated: Vec<Nsga2Entry<D>>,
|
||||
objectives: &crate::core::objective::ObjectiveSpace,
|
||||
evaluations: usize,
|
||||
generations: usize,
|
||||
) -> OptimizationResult<D> {
|
||||
let final_pop: Vec<Candidate<D>> = annotated.into_iter().map(|e| e.candidate).collect();
|
||||
let front = pareto_front(&final_pop, objectives);
|
||||
let best = best_candidate(&final_pop, objectives);
|
||||
OptimizationResult::new(
|
||||
Population::new(final_pop),
|
||||
front,
|
||||
best,
|
||||
evaluations,
|
||||
generations,
|
||||
)
|
||||
}
|
||||
|
||||
fn annotate<D: Clone>(
|
||||
population: Vec<Candidate<D>>,
|
||||
objectives: &crate::core::objective::ObjectiveSpace,
|
||||
|
||||
@@ -43,6 +43,44 @@ impl Default for Nsga3Config {
|
||||
}
|
||||
|
||||
/// NSGA-III optimizer.
|
||||
///
|
||||
/// NSGA-II's many-objective successor: replaces crowding distance with
|
||||
/// reference-point niching over Das–Dennis points in the normalized
|
||||
/// objective space. The canonical default for 4+ objectives.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = Nsga3::new(
|
||||
/// Nsga3Config {
|
||||
/// population_size: 30,
|
||||
/// generations: 20,
|
||||
/// reference_divisions: 12,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Nsga3<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -47,6 +47,36 @@ impl Default for OnePlusOneEsConfig {
|
||||
|
||||
/// (1+1)-ES with the one-fifth rule: tiny, parameter-light continuous
|
||||
/// optimizer. `Vec<f64>` decisions only; single-objective only.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = OnePlusOneEs::new(
|
||||
/// OnePlusOneEsConfig {
|
||||
/// iterations: 1_000,
|
||||
/// initial_sigma: 0.5,
|
||||
/// adaptation_period: 50,
|
||||
/// step_increase: 1.22,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0); 3]),
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// assert!(r.best.unwrap().evaluation.objectives[0] < 1e-3);
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct OnePlusOneEs {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -36,6 +36,31 @@ impl Default for PaesConfig {
|
||||
/// One current candidate, one mutation per iteration, one bounded archive.
|
||||
/// Intentionally a readable baseline rather than a research-perfect PAES
|
||||
/// (spec §12.2).
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = Paes::new(
|
||||
/// PaesConfig { iterations: 200, archive_size: 30, seed: 42 },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0)]),
|
||||
/// GaussianMutation { sigma: 0.3 },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Paes<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -55,6 +55,37 @@ impl Default for ParticleSwarmConfig {
|
||||
/// Velocities are clamped to `±(hi - lo)` per dimension to prevent
|
||||
/// "swarm explosion." Pair with `RealBounds` for both the search bounds
|
||||
/// and the initial particle positions.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = ParticleSwarm::new(
|
||||
/// ParticleSwarmConfig {
|
||||
/// swarm_size: 20,
|
||||
/// generations: 50,
|
||||
/// inertia: 0.7,
|
||||
/// cognitive: 1.5,
|
||||
/// social: 1.5,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0); 3]),
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// assert!(r.best.is_some());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ParticleSwarm {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -47,6 +47,41 @@ impl Default for PesaIIConfig {
|
||||
/// Maintains an internal population (used to drive variation) and an
|
||||
/// external non-dominated archive. Selection biases toward members in
|
||||
/// sparsely-populated grid boxes so the front spreads out.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = PesaII::new(
|
||||
/// PesaIIConfig {
|
||||
/// population_size: 20,
|
||||
/// archive_size: 30,
|
||||
/// generations: 20,
|
||||
/// grid_divisions: 8,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct PesaII<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -38,6 +38,31 @@ impl Default for RandomSearchConfig {
|
||||
/// Each iteration the configured `Initializer` produces `batch_size` decisions
|
||||
/// which are evaluated and pushed into the population. Cheap, parallelism-free,
|
||||
/// and useful as a sanity-check baseline.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = RandomSearch::new(
|
||||
/// RandomSearchConfig { iterations: 200, batch_size: 10, seed: 42 },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0); 3]),
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// assert_eq!(r.evaluations, 200 * 10);
|
||||
/// assert!(r.best.is_some());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct RandomSearch<I> {
|
||||
/// Algorithm configuration.
|
||||
@@ -63,28 +88,49 @@ where
|
||||
I: Initializer<P::Decision>,
|
||||
{
|
||||
fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision> {
|
||||
self.run_with(problem, &mut ())
|
||||
}
|
||||
|
||||
fn run_with<O>(&mut self, problem: &P, observer: &mut O) -> OptimizationResult<P::Decision>
|
||||
where
|
||||
O: crate::observer::Observer<P::Decision>,
|
||||
{
|
||||
use crate::observer::Snapshot;
|
||||
use std::ops::ControlFlow;
|
||||
|
||||
let objectives = problem.objectives();
|
||||
let mut rng = rng_from_seed(self.config.seed);
|
||||
let mut all: Vec<Candidate<P::Decision>> = Vec::new();
|
||||
let mut evaluations = 0usize;
|
||||
let started = std::time::Instant::now();
|
||||
let mut completed: usize = 0;
|
||||
|
||||
for _ in 0..self.config.iterations {
|
||||
for iteration in 1..=self.config.iterations {
|
||||
let decisions = self
|
||||
.initializer
|
||||
.initialize(self.config.batch_size, &mut rng);
|
||||
evaluations += decisions.len();
|
||||
all.extend(evaluate_batch(problem, decisions));
|
||||
completed = iteration;
|
||||
|
||||
let best = best_candidate(&all, &objectives);
|
||||
let snap = Snapshot {
|
||||
iteration,
|
||||
evaluations,
|
||||
elapsed: started.elapsed(),
|
||||
population: &all,
|
||||
pareto_front: None,
|
||||
best: best.as_ref(),
|
||||
objectives: &objectives,
|
||||
};
|
||||
if let ControlFlow::Break(()) = observer.observe(&snap) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
let front = pareto_front(&all, &objectives);
|
||||
let best = best_candidate(&all, &objectives);
|
||||
OptimizationResult::new(
|
||||
Population::new(all),
|
||||
front,
|
||||
best,
|
||||
evaluations,
|
||||
self.config.iterations,
|
||||
)
|
||||
OptimizationResult::new(Population::new(all), front, best, evaluations, completed)
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -41,6 +41,45 @@ impl Default for RveaConfig {
|
||||
}
|
||||
|
||||
/// Reference Vector-guided Evolutionary Algorithm.
|
||||
///
|
||||
/// Many-objective EA that uses Das–Dennis reference vectors with an
|
||||
/// adaptive penalty term to balance convergence and diversity as
|
||||
/// generations progress.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = Rvea::new(
|
||||
/// RveaConfig {
|
||||
/// population_size: 30,
|
||||
/// generations: 20,
|
||||
/// reference_divisions: 19,
|
||||
/// alpha: 2.0,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Rvea<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -41,6 +41,36 @@ impl Default for SimulatedAnnealingConfig {
|
||||
/// and `T` anneals geometrically from `initial_temperature` to
|
||||
/// `final_temperature` over the iteration count. Generic over decision
|
||||
/// type — pair with any `Variation` impl that returns one child per call.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = SimulatedAnnealing::new(
|
||||
/// SimulatedAnnealingConfig {
|
||||
/// iterations: 2_000,
|
||||
/// initial_temperature: 1.0,
|
||||
/// final_temperature: 1e-3,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0); 3]),
|
||||
/// GaussianMutation { sigma: 0.3 },
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// assert!(r.best.is_some());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct SimulatedAnnealing<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -49,6 +49,40 @@ impl Default for SmsEmoaConfig {
|
||||
/// non-dominated front. Excellent convergence quality at the price of
|
||||
/// quadratic-in-N hypervolume evaluations per generation, so practical
|
||||
/// up to ~4 objectives at population sizes ≤ 200.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = SmsEmoa::new(
|
||||
/// SmsEmoaConfig {
|
||||
/// population_size: 20,
|
||||
/// generations: 100,
|
||||
/// reference_point: vec![30.0, 30.0],
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct SmsEmoa<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -51,6 +51,37 @@ impl Default for SeparableNesConfig {
|
||||
/// following the natural gradient of expected fitness, with rank-shaped
|
||||
/// fitness utilities for invariance to monotone transforms of the
|
||||
/// objective.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = SeparableNes::new(
|
||||
/// SeparableNesConfig {
|
||||
/// population_size: 16,
|
||||
/// generations: 80,
|
||||
/// initial_sigma: 1.0,
|
||||
/// mean_learning_rate: 1.0,
|
||||
/// sigma_learning_rate: None, // use NES default
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0); 3]),
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// assert!(r.best.unwrap().evaluation.objectives[0] < 1e-3);
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct SeparableNes {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -37,6 +37,40 @@ impl Default for Spea2Config {
|
||||
}
|
||||
|
||||
/// SPEA2 optimizer.
|
||||
///
|
||||
/// Strength Pareto Evolutionary Algorithm 2: combines a strength-based
|
||||
/// dominance score with a k-th nearest-neighbor density estimate. Maintains
|
||||
/// an external archive separate from the working population.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Schaffer;
|
||||
/// impl Problem for Schaffer {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let bounds = vec![(-5.0_f64, 5.0_f64)];
|
||||
/// let mut opt = Spea2::new(
|
||||
/// Spea2Config { population_size: 30, archive_size: 30, generations: 20, seed: 42 },
|
||||
/// RealBounds::new(bounds.clone()),
|
||||
/// CompositeVariation {
|
||||
/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||||
/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||||
/// },
|
||||
/// );
|
||||
/// let r = opt.run(&Schaffer);
|
||||
/// assert_eq!(r.population.len(), 30);
|
||||
/// assert!(!r.pareto_front.is_empty());
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Spea2<I, V> {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -41,6 +41,30 @@ impl Default for TlboConfig {
|
||||
/// population_size and generations. Compared with the rest of heuropt's
|
||||
/// SO toolkit (DE has F+CR, PSO has w+c1+c2, CMA-ES has σ, GA needs
|
||||
/// crossover+mutation operators), TLBO works out of the box.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = Tlbo::new(
|
||||
/// TlboConfig { population_size: 20, generations: 50, seed: 42 },
|
||||
/// RealBounds::new(vec![(-5.0, 5.0); 3]),
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// assert!(r.best.unwrap().evaluation.objectives[0] < 1e-3);
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Tlbo {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -52,6 +52,37 @@ impl Default for TpeConfig {
|
||||
/// `BayesianOpt`, no GP — TPE models `p(x | y < y*)` and `p(x | y >= y*)`
|
||||
/// as per-axis Gaussian KDEs and picks the next candidate by maximizing
|
||||
/// the ratio of the two densities.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct Sphere;
|
||||
/// impl Problem for Sphere {
|
||||
/// type Decision = Vec<f64>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::minimize("f")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = Tpe::new(
|
||||
/// TpeConfig {
|
||||
/// initial_samples: 10,
|
||||
/// iterations: 50,
|
||||
/// good_fraction: 0.25,
|
||||
/// candidate_samples: 24,
|
||||
/// bandwidth_factor: 1.0,
|
||||
/// seed: 42,
|
||||
/// },
|
||||
/// RealBounds::new(vec![(-3.0, 3.0); 3]),
|
||||
/// );
|
||||
/// let r = opt.run(&Sphere);
|
||||
/// assert_eq!(r.evaluations, 60);
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Tpe {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -49,6 +49,34 @@ impl Default for UmdaConfig {
|
||||
/// `[1 / (2·selected_size), 1 - 1 / (2·selected_size)]` (Laplace-style
|
||||
/// smoothing) so the population never collapses to a single deterministic
|
||||
/// string.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
///
|
||||
/// struct OneMax;
|
||||
/// impl Problem for OneMax {
|
||||
/// type Decision = Vec<bool>;
|
||||
/// fn objectives(&self) -> ObjectiveSpace {
|
||||
/// ObjectiveSpace::new(vec![Objective::maximize("ones")])
|
||||
/// }
|
||||
/// fn evaluate(&self, x: &Vec<bool>) -> Evaluation {
|
||||
/// Evaluation::new(vec![x.iter().filter(|b| **b).count() as f64])
|
||||
/// }
|
||||
/// }
|
||||
///
|
||||
/// let mut opt = Umda::new(UmdaConfig {
|
||||
/// population_size: 50,
|
||||
/// selected_size: 20,
|
||||
/// generations: 30,
|
||||
/// bits: 16,
|
||||
/// seed: 42,
|
||||
/// });
|
||||
/// let r = opt.run(&OneMax);
|
||||
/// // OneMax with 16 bits: optimum is 16. UMDA should be very close.
|
||||
/// assert!(r.best.unwrap().evaluation.objectives[0] >= 14.0);
|
||||
/// ```
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Umda {
|
||||
/// Algorithm configuration.
|
||||
|
||||
@@ -34,6 +34,11 @@ impl<D> Population<D> {
|
||||
self.candidates.iter()
|
||||
}
|
||||
|
||||
/// View the candidates as a slice.
|
||||
pub fn as_slice(&self) -> &[Candidate<D>] {
|
||||
&self.candidates
|
||||
}
|
||||
|
||||
/// Unwrap into the inner `Vec<Candidate<D>>`.
|
||||
pub fn into_vec(self) -> Vec<Candidate<D>> {
|
||||
self.candidates
|
||||
|
||||
+31
-9
@@ -1,16 +1,37 @@
|
||||
//! `heuropt` — a practical Rust toolkit for implementing heuristic
|
||||
//! single-objective, multi-objective, and many-objective optimization
|
||||
//! algorithms.
|
||||
//! `heuropt` — a practical Rust toolkit for heuristic single-,
|
||||
//! multi-, and many-objective optimization.
|
||||
//!
|
||||
//! The crate aims to make three things obvious:
|
||||
//!
|
||||
//! 1. Define an optimization problem by implementing [`Problem`](crate::core::Problem).
|
||||
//! 2. Run a built-in optimizer such as [`Nsga2`](crate::algorithms::Nsga2) or
|
||||
//! [`RandomSearch`](crate::algorithms::RandomSearch).
|
||||
//! 3. Implement a new optimizer by implementing
|
||||
//! [`Optimizer`](crate::traits::Optimizer).
|
||||
//! 1. **Define a problem** by implementing [`Problem`](crate::core::Problem).
|
||||
//! 2. **Run a built-in optimizer** — pick from 35 algorithms in
|
||||
//! [`algorithms`] covering single-objective continuous (CMA-ES,
|
||||
//! Differential Evolution, Nelder-Mead, …), multi-objective
|
||||
//! (NSGA-II, MOPSO, IBEA, MOEA/D, …), many-objective (NSGA-III,
|
||||
//! GrEA, RVEA, …), and sample-efficient regimes (Bayesian
|
||||
//! Optimization, TPE, Hyperband).
|
||||
//! 3. **Or implement your own** by implementing
|
||||
//! [`Optimizer`](crate::traits::Optimizer). The trait is one
|
||||
//! method long.
|
||||
//!
|
||||
//! See `docs/heuropt_tech_design_spec.md` for the full design rationale.
|
||||
//! ## Where to read more
|
||||
//!
|
||||
//! - **User guide / cookbook / comparison vs pymoo & friends:**
|
||||
//! <https://swaits.github.io/heuropt/>.
|
||||
//! - **Algorithm selection:** the README's decision tree, or the
|
||||
//! "Choosing an algorithm" book chapter.
|
||||
//! - **Design rationale:** `docs/heuropt_tech_design_spec.md` in the
|
||||
//! repository.
|
||||
//!
|
||||
//! ## Optional features
|
||||
//!
|
||||
//! - `serde` — derives `Serialize` / `Deserialize` on the core data
|
||||
//! types ([`Candidate`](crate::core::Candidate),
|
||||
//! [`Population`](crate::core::Population),
|
||||
//! [`Evaluation`](crate::core::Evaluation), …).
|
||||
//! - `parallel` — rayon-backed parallel population evaluation in
|
||||
//! every population-based algorithm. Seeded runs stay bit-
|
||||
//! identical to serial mode.
|
||||
//!
|
||||
//! # Quick example
|
||||
//!
|
||||
@@ -47,6 +68,7 @@ pub mod algorithms;
|
||||
pub mod core;
|
||||
pub(crate) mod internal;
|
||||
pub mod metrics;
|
||||
pub mod observer;
|
||||
pub mod operators;
|
||||
pub mod pareto;
|
||||
pub mod prelude;
|
||||
|
||||
@@ -0,0 +1,207 @@
|
||||
//! Inverted Generational Distance (IGD) and IGD+ performance indicators.
|
||||
//!
|
||||
//! Both quantify how well an approximation set covers a reference set
|
||||
//! (typically the true Pareto front). Smaller values are better.
|
||||
|
||||
use crate::core::candidate::Candidate;
|
||||
use crate::core::evaluation::Evaluation;
|
||||
use crate::core::objective::ObjectiveSpace;
|
||||
|
||||
/// Inverted Generational Distance.
|
||||
///
|
||||
/// For each point in the `reference` set, compute the Euclidean distance
|
||||
/// to its nearest neighbor in the `approximation` set (in minimization-
|
||||
/// oriented objective space), then average:
|
||||
///
|
||||
/// ```text
|
||||
/// IGD(A) = (1 / |R|) · Σ_{r ∈ R} min_{a ∈ A} ‖a − r‖₂
|
||||
/// ```
|
||||
///
|
||||
/// Lower is better. IGD captures both convergence (close to the front)
|
||||
/// and spread (the approximation must cover the reference).
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// If `reference` is empty.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
/// use heuropt::metrics::igd::igd;
|
||||
///
|
||||
/// let space = ObjectiveSpace::new(vec![
|
||||
/// Objective::minimize("f1"),
|
||||
/// Objective::minimize("f2"),
|
||||
/// ]);
|
||||
/// // Approximation: a sparse 2-point front.
|
||||
/// let approx = [
|
||||
/// Candidate::new((), Evaluation::new(vec![0.0, 1.0])),
|
||||
/// Candidate::new((), Evaluation::new(vec![1.0, 0.0])),
|
||||
/// ];
|
||||
/// // Reference: a dense 3-point sample of the true front.
|
||||
/// let reference = [
|
||||
/// Evaluation::new(vec![0.0, 1.0]),
|
||||
/// Evaluation::new(vec![0.5, 0.5]),
|
||||
/// Evaluation::new(vec![1.0, 0.0]),
|
||||
/// ];
|
||||
/// let v = igd(&approx, &reference, &space);
|
||||
/// // The middle reference point is unfortunately distance √(0.5²+0.5²) = 0.707
|
||||
/// // from each approximation point; the boundary points are 0 away.
|
||||
/// // IGD = (0 + 0.707 + 0) / 3 ≈ 0.236.
|
||||
/// assert!((v - 0.2357).abs() < 1e-3);
|
||||
/// ```
|
||||
pub fn igd<D>(
|
||||
approximation: &[Candidate<D>],
|
||||
reference: &[Evaluation],
|
||||
objectives: &ObjectiveSpace,
|
||||
) -> f64 {
|
||||
assert!(
|
||||
!reference.is_empty(),
|
||||
"igd: reference set must not be empty"
|
||||
);
|
||||
let approx_oriented: Vec<Vec<f64>> = approximation
|
||||
.iter()
|
||||
.map(|c| objectives.as_minimization(&c.evaluation.objectives))
|
||||
.collect();
|
||||
if approx_oriented.is_empty() {
|
||||
return f64::INFINITY;
|
||||
}
|
||||
let mut total = 0.0_f64;
|
||||
for r in reference {
|
||||
let r_oriented = objectives.as_minimization(&r.objectives);
|
||||
let mut min_d = f64::INFINITY;
|
||||
for a in &approx_oriented {
|
||||
let d: f64 = a
|
||||
.iter()
|
||||
.zip(r_oriented.iter())
|
||||
.map(|(x, y)| (x - y).powi(2))
|
||||
.sum::<f64>()
|
||||
.sqrt();
|
||||
if d < min_d {
|
||||
min_d = d;
|
||||
}
|
||||
}
|
||||
total += min_d;
|
||||
}
|
||||
total / reference.len() as f64
|
||||
}
|
||||
|
||||
/// IGD+ — a dominance-respecting variant of IGD.
|
||||
///
|
||||
/// For each reference point `r`, the distance to an approximation
|
||||
/// point `a` is computed only on objectives where `a` is *worse than*
|
||||
/// `r` — i.e. on the "violation" component of the gap. This makes
|
||||
/// IGD+ a Pareto-compliant indicator: adding a dominated point to the
|
||||
/// approximation never improves the score.
|
||||
///
|
||||
/// ```text
|
||||
/// IGD+(A) = (1 / |R|) · Σ_{r ∈ R} min_{a ∈ A} ‖max(a − r, 0)‖₂
|
||||
/// ```
|
||||
///
|
||||
/// Lower is better.
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// If `reference` is empty.
|
||||
pub fn igd_plus<D>(
|
||||
approximation: &[Candidate<D>],
|
||||
reference: &[Evaluation],
|
||||
objectives: &ObjectiveSpace,
|
||||
) -> f64 {
|
||||
assert!(
|
||||
!reference.is_empty(),
|
||||
"igd_plus: reference set must not be empty"
|
||||
);
|
||||
let approx_oriented: Vec<Vec<f64>> = approximation
|
||||
.iter()
|
||||
.map(|c| objectives.as_minimization(&c.evaluation.objectives))
|
||||
.collect();
|
||||
if approx_oriented.is_empty() {
|
||||
return f64::INFINITY;
|
||||
}
|
||||
let mut total = 0.0_f64;
|
||||
for r in reference {
|
||||
let r_oriented = objectives.as_minimization(&r.objectives);
|
||||
let mut min_d = f64::INFINITY;
|
||||
for a in &approx_oriented {
|
||||
let d: f64 = a
|
||||
.iter()
|
||||
.zip(r_oriented.iter())
|
||||
.map(|(x, y)| (x - y).max(0.0).powi(2))
|
||||
.sum::<f64>()
|
||||
.sqrt();
|
||||
if d < min_d {
|
||||
min_d = d;
|
||||
}
|
||||
}
|
||||
total += min_d;
|
||||
}
|
||||
total / reference.len() as f64
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::core::objective::Objective;
|
||||
|
||||
fn space_min2() -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
}
|
||||
|
||||
fn cand(obj: Vec<f64>) -> Candidate<()> {
|
||||
Candidate::new((), Evaluation::new(obj))
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn igd_perfect_match_is_zero() {
|
||||
let s = space_min2();
|
||||
let approx = [cand(vec![0.0, 1.0]), cand(vec![1.0, 0.0])];
|
||||
let reference = [
|
||||
Evaluation::new(vec![0.0, 1.0]),
|
||||
Evaluation::new(vec![1.0, 0.0]),
|
||||
];
|
||||
let v = igd(&approx, &reference, &s);
|
||||
assert!(v < 1e-12);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn igd_known_value() {
|
||||
let s = space_min2();
|
||||
let approx = [cand(vec![0.0, 0.0])];
|
||||
let reference = [Evaluation::new(vec![1.0, 1.0])];
|
||||
let v = igd(&approx, &reference, &s);
|
||||
assert!((v - 2.0_f64.sqrt()).abs() < 1e-12);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn igd_plus_dominated_point_does_not_improve() {
|
||||
let s = space_min2();
|
||||
let reference = [
|
||||
Evaluation::new(vec![0.0, 1.0]),
|
||||
Evaluation::new(vec![1.0, 0.0]),
|
||||
];
|
||||
let base = vec![cand(vec![0.5, 0.5])];
|
||||
let with_dominated = vec![cand(vec![0.5, 0.5]), cand(vec![1.0, 1.0])];
|
||||
let v_base = igd_plus(&base, &reference, &s);
|
||||
let v_with = igd_plus(&with_dominated, &reference, &s);
|
||||
// Adding a dominated point should not improve the score.
|
||||
assert!(v_with >= v_base - 1e-12);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn igd_empty_approximation_is_infinity() {
|
||||
let s = space_min2();
|
||||
let approx: [Candidate<()>; 0] = [];
|
||||
let reference = [Evaluation::new(vec![0.0, 1.0])];
|
||||
assert!(igd(&approx, &reference, &s).is_infinite());
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "reference set must not be empty")]
|
||||
fn igd_empty_reference_panics() {
|
||||
let s = space_min2();
|
||||
let approx = [cand(vec![0.0, 1.0])];
|
||||
let _ = igd::<()>(&approx, &[], &s);
|
||||
}
|
||||
}
|
||||
@@ -1,7 +1,11 @@
|
||||
//! Quality metrics for Pareto fronts.
|
||||
|
||||
pub mod hypervolume;
|
||||
pub mod igd;
|
||||
pub mod r2;
|
||||
pub mod spacing;
|
||||
|
||||
pub use hypervolume::*;
|
||||
pub use igd::{igd, igd_plus};
|
||||
pub use r2::r2;
|
||||
pub use spacing::*;
|
||||
|
||||
@@ -0,0 +1,173 @@
|
||||
//! R2 indicator — a unary quality measure for Pareto fronts.
|
||||
//!
|
||||
//! For each weight vector `λ` in a user-supplied set, find the
|
||||
//! best (smallest) weighted Tchebycheff value across the front;
|
||||
//! average over all weight vectors. Lower is better.
|
||||
|
||||
use crate::core::candidate::Candidate;
|
||||
use crate::core::objective::ObjectiveSpace;
|
||||
|
||||
/// R2 indicator using the weighted Tchebycheff utility.
|
||||
///
|
||||
/// ```text
|
||||
/// R2(A) = (1 / |Λ|) · Σ_{λ ∈ Λ} min_{a ∈ A} max_i { λ_i · |a_i − z*_i| }
|
||||
/// ```
|
||||
///
|
||||
/// where `z*` is the ideal point (per-axis minimum across the
|
||||
/// approximation, in minimization-oriented coordinates) and `Λ` is
|
||||
/// a set of unit-simplex weight vectors. Lower is better.
|
||||
///
|
||||
/// Use [`das_dennis`](crate::pareto::das_dennis) to generate the
|
||||
/// canonical structured weight set.
|
||||
///
|
||||
/// # Panics
|
||||
///
|
||||
/// If the approximation is empty, or any weight vector has wrong
|
||||
/// length / negative entries / zero sum.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
/// use heuropt::metrics::r2::r2;
|
||||
///
|
||||
/// let space = ObjectiveSpace::new(vec![
|
||||
/// Objective::minimize("f1"),
|
||||
/// Objective::minimize("f2"),
|
||||
/// ]);
|
||||
/// let approx = [
|
||||
/// Candidate::new((), Evaluation::new(vec![0.0, 1.0])),
|
||||
/// Candidate::new((), Evaluation::new(vec![1.0, 0.0])),
|
||||
/// ];
|
||||
/// // Two weight vectors: (1, 0) and (0, 1) — extreme directions.
|
||||
/// let weights = [vec![1.0, 0.0], vec![0.0, 1.0]];
|
||||
/// let v = r2(&approx, &weights, &space);
|
||||
/// // For each direction, the best front member matches that axis exactly.
|
||||
/// // R2 = 0 since the ideal point is achieved on each direction.
|
||||
/// assert!(v < 1e-12);
|
||||
/// ```
|
||||
pub fn r2<D>(
|
||||
approximation: &[Candidate<D>],
|
||||
weights: &[Vec<f64>],
|
||||
objectives: &ObjectiveSpace,
|
||||
) -> f64 {
|
||||
assert!(
|
||||
!approximation.is_empty(),
|
||||
"r2: approximation must not be empty"
|
||||
);
|
||||
assert!(!weights.is_empty(), "r2: weight set must not be empty");
|
||||
let m = objectives.len();
|
||||
for (i, w) in weights.iter().enumerate() {
|
||||
assert_eq!(
|
||||
w.len(),
|
||||
m,
|
||||
"r2: weight {i} has wrong length ({} vs {m})",
|
||||
w.len()
|
||||
);
|
||||
assert!(
|
||||
w.iter().all(|&v| v >= 0.0),
|
||||
"r2: weight {i} has a negative entry"
|
||||
);
|
||||
assert!(w.iter().sum::<f64>() > 0.0, "r2: weight {i} has zero sum");
|
||||
}
|
||||
|
||||
// Convert all approximation members to minimization orientation once.
|
||||
let oriented: Vec<Vec<f64>> = approximation
|
||||
.iter()
|
||||
.map(|c| objectives.as_minimization(&c.evaluation.objectives))
|
||||
.collect();
|
||||
|
||||
// Ideal point z* (per-axis minimum).
|
||||
let mut z_star = vec![f64::INFINITY; m];
|
||||
for o in &oriented {
|
||||
for k in 0..m {
|
||||
if o[k] < z_star[k] {
|
||||
z_star[k] = o[k];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let mut total = 0.0_f64;
|
||||
for w in weights {
|
||||
let mut best = f64::INFINITY;
|
||||
for o in &oriented {
|
||||
// Weighted Tchebycheff: max_i { w_i · |o_i − z*_i| }
|
||||
let mut t = 0.0_f64;
|
||||
for k in 0..m {
|
||||
let dk = (o[k] - z_star[k]).abs() * w[k];
|
||||
if dk > t {
|
||||
t = dk;
|
||||
}
|
||||
}
|
||||
if t < best {
|
||||
best = t;
|
||||
}
|
||||
}
|
||||
total += best;
|
||||
}
|
||||
total / weights.len() as f64
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::core::evaluation::Evaluation;
|
||||
use crate::core::objective::Objective;
|
||||
use crate::pareto::das_dennis;
|
||||
|
||||
fn space_min2() -> ObjectiveSpace {
|
||||
ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
|
||||
}
|
||||
|
||||
fn cand(obj: Vec<f64>) -> Candidate<()> {
|
||||
Candidate::new((), Evaluation::new(obj))
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn r2_extremes_are_perfect_at_endpoints() {
|
||||
let s = space_min2();
|
||||
let front = [cand(vec![0.0, 1.0]), cand(vec![1.0, 0.0])];
|
||||
let weights = [vec![1.0, 0.0], vec![0.0, 1.0]];
|
||||
assert!(r2(&front, &weights, &s) < 1e-12);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn r2_dense_dasdennis_finite_for_uniform_front() {
|
||||
let s = space_min2();
|
||||
let weights = das_dennis(2, 5);
|
||||
let front: Vec<Candidate<()>> = (0..=10)
|
||||
.map(|i| {
|
||||
let t = i as f64 / 10.0;
|
||||
cand(vec![t, 1.0 - t])
|
||||
})
|
||||
.collect();
|
||||
let v = r2(&front, &weights, &s);
|
||||
assert!(v.is_finite());
|
||||
assert!(v >= 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "approximation must not be empty")]
|
||||
fn r2_empty_approximation_panics() {
|
||||
let s = space_min2();
|
||||
let weights = vec![vec![1.0, 0.0]];
|
||||
let _: f64 = r2::<()>(&[], &weights, &s);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "weight set must not be empty")]
|
||||
fn r2_empty_weights_panics() {
|
||||
let s = space_min2();
|
||||
let front = [cand(vec![0.0, 1.0])];
|
||||
let _ = r2(&front, &[], &s);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "wrong length")]
|
||||
fn r2_wrong_dim_weight_panics() {
|
||||
let s = space_min2();
|
||||
let front = [cand(vec![0.0, 1.0])];
|
||||
let weights = vec![vec![1.0, 0.0, 0.0]];
|
||||
let _ = r2(&front, &weights, &s);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,418 @@
|
||||
//! Built-in observers covering the common stop conditions.
|
||||
|
||||
use std::ops::ControlFlow;
|
||||
use std::time::Duration;
|
||||
|
||||
use super::{Observer, Snapshot};
|
||||
use crate::core::objective::Direction;
|
||||
|
||||
/// Halt after a fixed wall-clock duration since `run_with` started.
|
||||
///
|
||||
/// # Example
|
||||
///
|
||||
/// ```
|
||||
/// use heuropt::prelude::*;
|
||||
/// use std::time::Duration;
|
||||
///
|
||||
/// let stop = MaxTime::new(Duration::from_millis(50));
|
||||
/// // pass `&mut stop` to `Optimizer::run_with`.
|
||||
/// # let _ = stop;
|
||||
/// ```
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct MaxTime {
|
||||
pub limit: Duration,
|
||||
}
|
||||
|
||||
impl MaxTime {
|
||||
pub fn new(limit: Duration) -> Self {
|
||||
Self { limit }
|
||||
}
|
||||
}
|
||||
|
||||
impl<D> Observer<D> for MaxTime {
|
||||
#[inline]
|
||||
fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
|
||||
if snap.elapsed >= self.limit {
|
||||
ControlFlow::Break(())
|
||||
} else {
|
||||
ControlFlow::Continue(())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Halt after a target number of generations.
|
||||
///
|
||||
/// Most algorithms already take a `generations` count in their config,
|
||||
/// so this is mostly useful for capping algorithms whose configured
|
||||
/// loop is open-ended (or for testing).
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct MaxIterations {
|
||||
pub limit: usize,
|
||||
}
|
||||
|
||||
impl MaxIterations {
|
||||
pub fn new(limit: usize) -> Self {
|
||||
Self { limit }
|
||||
}
|
||||
}
|
||||
|
||||
impl<D> Observer<D> for MaxIterations {
|
||||
#[inline]
|
||||
fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
|
||||
if snap.iteration >= self.limit {
|
||||
ControlFlow::Break(())
|
||||
} else {
|
||||
ControlFlow::Continue(())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Halt as soon as the best single-objective fitness reaches `target`.
|
||||
///
|
||||
/// Direction-aware: for `Minimize` axes the target is reached when
|
||||
/// `best ≤ target`; for `Maximize`, when `best ≥ target`.
|
||||
///
|
||||
/// Multi-objective snapshots (where `Snapshot::best` is `None` or the
|
||||
/// problem has more than one objective) are silently ignored — this
|
||||
/// observer never breaks them.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct TargetFitness {
|
||||
pub target: f64,
|
||||
}
|
||||
|
||||
impl TargetFitness {
|
||||
pub fn new(target: f64) -> Self {
|
||||
Self { target }
|
||||
}
|
||||
}
|
||||
|
||||
impl<D> Observer<D> for TargetFitness {
|
||||
fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
|
||||
if !snap.objectives.is_single_objective() {
|
||||
return ControlFlow::Continue(());
|
||||
}
|
||||
let direction = snap.objectives.objectives[0].direction;
|
||||
if let Some(best) = snap.best
|
||||
&& let Some(&v) = best.evaluation.objectives.first()
|
||||
{
|
||||
let hit = match direction {
|
||||
Direction::Minimize => v <= self.target,
|
||||
Direction::Maximize => v >= self.target,
|
||||
};
|
||||
if hit {
|
||||
return ControlFlow::Break(());
|
||||
}
|
||||
}
|
||||
ControlFlow::Continue(())
|
||||
}
|
||||
}
|
||||
|
||||
/// Halt when the best single-objective fitness has not improved by
|
||||
/// more than `tolerance` over the last `window` generations.
|
||||
///
|
||||
/// Multi-objective snapshots are silently ignored.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct Stagnation {
|
||||
pub window: usize,
|
||||
pub tolerance: f64,
|
||||
history: std::collections::VecDeque<f64>,
|
||||
}
|
||||
|
||||
impl Stagnation {
|
||||
pub fn new(window: usize, tolerance: f64) -> Self {
|
||||
assert!(window > 0, "Stagnation window must be > 0");
|
||||
assert!(
|
||||
tolerance >= 0.0,
|
||||
"Stagnation tolerance must be non-negative"
|
||||
);
|
||||
Self {
|
||||
window,
|
||||
tolerance,
|
||||
history: std::collections::VecDeque::with_capacity(window + 1),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<D> Observer<D> for Stagnation {
|
||||
fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
|
||||
if !snap.objectives.is_single_objective() {
|
||||
return ControlFlow::Continue(());
|
||||
}
|
||||
let direction = snap.objectives.objectives[0].direction;
|
||||
let v = match snap
|
||||
.best
|
||||
.and_then(|c| c.evaluation.objectives.first().copied())
|
||||
{
|
||||
Some(v) => v,
|
||||
None => return ControlFlow::Continue(()),
|
||||
};
|
||||
// Push to history; cap at window+1 so we always have 1 + window samples.
|
||||
self.history.push_back(v);
|
||||
while self.history.len() > self.window + 1 {
|
||||
self.history.pop_front();
|
||||
}
|
||||
if self.history.len() <= self.window {
|
||||
return ControlFlow::Continue(());
|
||||
}
|
||||
let oldest = self.history.front().copied().unwrap();
|
||||
let newest = self.history.back().copied().unwrap();
|
||||
let improvement = match direction {
|
||||
Direction::Minimize => oldest - newest,
|
||||
Direction::Maximize => newest - oldest,
|
||||
};
|
||||
if improvement <= self.tolerance {
|
||||
ControlFlow::Break(())
|
||||
} else {
|
||||
ControlFlow::Continue(())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Compose two observers — break if **either** signals a break.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyOf<A, B> {
|
||||
pub a: A,
|
||||
pub b: B,
|
||||
}
|
||||
|
||||
impl<D, A, B> Observer<D> for AnyOf<A, B>
|
||||
where
|
||||
A: Observer<D>,
|
||||
B: Observer<D>,
|
||||
{
|
||||
fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
|
||||
// Always poll both so stateful observers (Stagnation) update
|
||||
// their history, then OR the results.
|
||||
let ra = self.a.observe(snap);
|
||||
let rb = self.b.observe(snap);
|
||||
if ra.is_break() || rb.is_break() {
|
||||
ControlFlow::Break(())
|
||||
} else {
|
||||
ControlFlow::Continue(())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Compose two observers — break only if **both** signal a break in
|
||||
/// the same call.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AllOf<A, B> {
|
||||
pub a: A,
|
||||
pub b: B,
|
||||
}
|
||||
|
||||
impl<D, A, B> Observer<D> for AllOf<A, B>
|
||||
where
|
||||
A: Observer<D>,
|
||||
B: Observer<D>,
|
||||
{
|
||||
fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
|
||||
let ra = self.a.observe(snap);
|
||||
let rb = self.b.observe(snap);
|
||||
if ra.is_break() && rb.is_break() {
|
||||
ControlFlow::Break(())
|
||||
} else {
|
||||
ControlFlow::Continue(())
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Call a user closure every `every` generations (default 1 = every
|
||||
/// generation). Useful for periodic logging without bloating callback
|
||||
/// frequency.
|
||||
pub struct Periodic<F> {
|
||||
pub every: usize,
|
||||
counter: usize,
|
||||
pub callback: F,
|
||||
}
|
||||
|
||||
impl<F> Periodic<F> {
|
||||
pub fn new(every: usize, callback: F) -> Self {
|
||||
assert!(every >= 1, "Periodic every must be >= 1");
|
||||
Self {
|
||||
every,
|
||||
counter: 0,
|
||||
callback,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<D, F> Observer<D> for Periodic<F>
|
||||
where
|
||||
F: FnMut(&Snapshot<'_, D>),
|
||||
{
|
||||
fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
|
||||
self.counter += 1;
|
||||
if self.counter >= self.every {
|
||||
self.counter = 0;
|
||||
(self.callback)(snap);
|
||||
}
|
||||
ControlFlow::Continue(())
|
||||
}
|
||||
}
|
||||
|
||||
/// Tracing-backed observer — emits a structured `debug!` event per
|
||||
/// generation with iteration / evaluations / elapsed / best fitness.
|
||||
///
|
||||
/// Available only with the `tracing` feature.
|
||||
#[cfg(feature = "tracing")]
|
||||
#[derive(Debug, Default, Clone, Copy)]
|
||||
pub struct TracingObserver;
|
||||
|
||||
#[cfg(feature = "tracing")]
|
||||
impl<D> Observer<D> for TracingObserver {
|
||||
fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
|
||||
let best = snap
|
||||
.best
|
||||
.and_then(|c| c.evaluation.objectives.first().copied());
|
||||
tracing::debug!(
|
||||
iteration = snap.iteration,
|
||||
evaluations = snap.evaluations,
|
||||
elapsed_ms = snap.elapsed.as_millis() as u64,
|
||||
best = ?best,
|
||||
front_size = snap.pareto_front.map(|f| f.len()),
|
||||
"heuropt generation",
|
||||
);
|
||||
ControlFlow::Continue(())
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::core::candidate::Candidate;
|
||||
use crate::core::evaluation::Evaluation;
|
||||
use crate::core::objective::{Objective, ObjectiveSpace};
|
||||
|
||||
fn snap_with_best<'a>(
|
||||
iteration: usize,
|
||||
elapsed_ms: u64,
|
||||
best: Option<&'a Candidate<()>>,
|
||||
objectives: &'a ObjectiveSpace,
|
||||
empty_pop: &'a [Candidate<()>],
|
||||
) -> Snapshot<'a, ()> {
|
||||
Snapshot {
|
||||
iteration,
|
||||
evaluations: 0,
|
||||
elapsed: Duration::from_millis(elapsed_ms),
|
||||
population: empty_pop,
|
||||
pareto_front: None,
|
||||
best,
|
||||
objectives,
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn max_time_breaks_after_limit() {
|
||||
let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
|
||||
let pop: Vec<Candidate<()>> = vec![];
|
||||
let mut o = MaxTime::new(Duration::from_millis(100));
|
||||
let s = snap_with_best(0, 50, None, &space, &pop);
|
||||
assert!(o.observe(&s).is_continue());
|
||||
let s = snap_with_best(1, 100, None, &space, &pop);
|
||||
assert!(o.observe(&s).is_break());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn target_fitness_minimize() {
|
||||
let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
|
||||
let pop: Vec<Candidate<()>> = vec![];
|
||||
let cand = Candidate::new((), Evaluation::new(vec![0.005]));
|
||||
let mut o = TargetFitness::new(0.01);
|
||||
let s = snap_with_best(0, 0, Some(&cand), &space, &pop);
|
||||
assert!(o.observe(&s).is_break());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn target_fitness_maximize() {
|
||||
let space = ObjectiveSpace::new(vec![Objective::maximize("f")]);
|
||||
let pop: Vec<Candidate<()>> = vec![];
|
||||
let cand_below = Candidate::new((), Evaluation::new(vec![0.5]));
|
||||
let cand_above = Candidate::new((), Evaluation::new(vec![1.5]));
|
||||
let mut o = TargetFitness::new(1.0);
|
||||
let s = snap_with_best(0, 0, Some(&cand_below), &space, &pop);
|
||||
assert!(o.observe(&s).is_continue());
|
||||
let s = snap_with_best(1, 0, Some(&cand_above), &space, &pop);
|
||||
assert!(o.observe(&s).is_break());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn stagnation_breaks_on_no_improvement() {
|
||||
let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
|
||||
let pop: Vec<Candidate<()>> = vec![];
|
||||
let mut o = Stagnation::new(3, 1e-6);
|
||||
|
||||
// Five generations of "no improvement" — same value every time.
|
||||
for i in 0..3 {
|
||||
let cand = Candidate::new((), Evaluation::new(vec![1.0]));
|
||||
let s = snap_with_best(i, 0, Some(&cand), &space, &pop);
|
||||
// First `window` calls just fill history; should not break.
|
||||
assert!(o.observe(&s).is_continue());
|
||||
}
|
||||
let cand = Candidate::new((), Evaluation::new(vec![1.0]));
|
||||
let s = snap_with_best(3, 0, Some(&cand), &space, &pop);
|
||||
assert!(o.observe(&s).is_break());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn stagnation_does_not_break_on_improvement() {
|
||||
let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
|
||||
let pop: Vec<Candidate<()>> = vec![];
|
||||
let mut o = Stagnation::new(2, 1e-6);
|
||||
let values = [1.0, 0.9, 0.8, 0.7];
|
||||
for (i, &v) in values.iter().enumerate() {
|
||||
let cand = Candidate::new((), Evaluation::new(vec![v]));
|
||||
let s = snap_with_best(i, 0, Some(&cand), &space, &pop);
|
||||
assert!(o.observe(&s).is_continue(), "iter {i}");
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn anyof_breaks_when_either_breaks() {
|
||||
let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
|
||||
let pop: Vec<Candidate<()>> = vec![];
|
||||
let cand = Candidate::new((), Evaluation::new(vec![5.0]));
|
||||
let mut o =
|
||||
<MaxIterations as Observer<()>>::or(MaxIterations::new(3), TargetFitness::new(1.0));
|
||||
for i in 0..3 {
|
||||
let s = snap_with_best(i, 0, Some(&cand), &space, &pop);
|
||||
assert!(o.observe(&s).is_continue(), "iter {i}");
|
||||
}
|
||||
// iteration = 3 hits MaxIterations limit → break
|
||||
let s = snap_with_best(3, 0, Some(&cand), &space, &pop);
|
||||
assert!(o.observe(&s).is_break());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn periodic_calls_callback_every_n() {
|
||||
let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
|
||||
let pop: Vec<Candidate<()>> = vec![];
|
||||
let mut count = 0_usize;
|
||||
{
|
||||
let mut o = Periodic::new(3, |_: &Snapshot<'_, ()>| count += 1);
|
||||
for i in 0..10 {
|
||||
let s = snap_with_best(i, 0, None, &space, &pop);
|
||||
let _ = o.observe(&s);
|
||||
}
|
||||
}
|
||||
assert_eq!(count, 3); // every 3rd of 10 = generations 2, 5, 8
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn closure_implements_observer() {
|
||||
let space = ObjectiveSpace::new(vec![Objective::minimize("f")]);
|
||||
let pop: Vec<Candidate<()>> = vec![];
|
||||
let mut count = 0_usize;
|
||||
let mut closure = |_: &Snapshot<'_, ()>| -> ControlFlow<()> {
|
||||
count += 1;
|
||||
if count >= 2 {
|
||||
ControlFlow::Break(())
|
||||
} else {
|
||||
ControlFlow::Continue(())
|
||||
}
|
||||
};
|
||||
let s = snap_with_best(0, 0, None, &space, &pop);
|
||||
assert!(<_ as Observer<()>>::observe(&mut closure, &s).is_continue());
|
||||
assert!(<_ as Observer<()>>::observe(&mut closure, &s).is_break());
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,101 @@
|
||||
//! Per-generation observation, callbacks, and stop conditions.
|
||||
//!
|
||||
//! Algorithms accept an [`Observer`] via [`Optimizer::run_with`] and call
|
||||
//! it once per generation (where "generation" makes sense for that
|
||||
//! algorithm — see each algorithm's docs). Returning
|
||||
//! [`std::ops::ControlFlow::Break`] from an observer halts the optimizer
|
||||
//! and the partial [`OptimizationResult`] is returned to the caller.
|
||||
//!
|
||||
//! Observers can be composed with [`builtin::AnyOf`] / [`builtin::AllOf`].
|
||||
//!
|
||||
//! [`OptimizationResult`]: crate::core::result::OptimizationResult
|
||||
//! [`Optimizer::run_with`]: crate::traits::Optimizer::run_with
|
||||
|
||||
pub mod builtin;
|
||||
mod snapshot;
|
||||
|
||||
pub use snapshot::Snapshot;
|
||||
|
||||
use std::ops::ControlFlow;
|
||||
|
||||
use crate::core::candidate::Candidate;
|
||||
use crate::core::objective::ObjectiveSpace;
|
||||
|
||||
/// A callback invoked by an [`Optimizer`](crate::traits::Optimizer)
|
||||
/// after every generation. Return [`ControlFlow::Break`] to halt
|
||||
/// the optimizer; [`ControlFlow::Continue`] to keep going.
|
||||
///
|
||||
/// Implement directly for stateful observers that need to track
|
||||
/// history (e.g. stagnation detection, convergence trace logging).
|
||||
/// For simple stop conditions, use the helpers in
|
||||
/// [`builtin`](crate::observer::builtin).
|
||||
pub trait Observer<D> {
|
||||
/// Inspect the latest snapshot. Return [`ControlFlow::Break`] to
|
||||
/// halt the run; [`ControlFlow::Continue`] to keep going.
|
||||
fn observe(&mut self, snapshot: &Snapshot<'_, D>) -> ControlFlow<()>;
|
||||
|
||||
/// Compose with another observer that fires when *either* of them
|
||||
/// signals a break.
|
||||
fn or<O: Observer<D>>(self, other: O) -> builtin::AnyOf<Self, O>
|
||||
where
|
||||
Self: Sized,
|
||||
{
|
||||
builtin::AnyOf { a: self, b: other }
|
||||
}
|
||||
|
||||
/// Compose with another observer that fires when *both* of them
|
||||
/// signal a break in the same call.
|
||||
fn and<O: Observer<D>>(self, other: O) -> builtin::AllOf<Self, O>
|
||||
where
|
||||
Self: Sized,
|
||||
{
|
||||
builtin::AllOf { a: self, b: other }
|
||||
}
|
||||
}
|
||||
|
||||
/// `()` is the no-op observer. Used as the default when callers don't
|
||||
/// want any callbacks (it's what `run` uses internally).
|
||||
impl<D> Observer<D> for () {
|
||||
#[inline]
|
||||
fn observe(&mut self, _: &Snapshot<'_, D>) -> ControlFlow<()> {
|
||||
ControlFlow::Continue(())
|
||||
}
|
||||
}
|
||||
|
||||
/// Closures of the right shape implement Observer too — short-form
|
||||
/// for one-liner callbacks.
|
||||
impl<D, F> Observer<D> for F
|
||||
where
|
||||
F: FnMut(&Snapshot<'_, D>) -> ControlFlow<()>,
|
||||
{
|
||||
#[inline]
|
||||
fn observe(&mut self, snap: &Snapshot<'_, D>) -> ControlFlow<()> {
|
||||
self(snap)
|
||||
}
|
||||
}
|
||||
|
||||
/// Build a snapshot for the "final notification" path of the default
|
||||
/// `run_with` impl on [`Optimizer`](crate::traits::Optimizer).
|
||||
///
|
||||
/// Algorithm impls that override `run_with` to call the observer per
|
||||
/// generation should construct their own snapshots inline rather than
|
||||
/// using this helper, because they have richer per-generation state.
|
||||
pub fn finalize_snapshot<'a, D>(
|
||||
iteration: usize,
|
||||
evaluations: usize,
|
||||
elapsed: std::time::Duration,
|
||||
population: &'a [Candidate<D>],
|
||||
pareto_front: Option<&'a [Candidate<D>]>,
|
||||
best: Option<&'a Candidate<D>>,
|
||||
objectives: &'a ObjectiveSpace,
|
||||
) -> Snapshot<'a, D> {
|
||||
Snapshot {
|
||||
iteration,
|
||||
evaluations,
|
||||
elapsed,
|
||||
population,
|
||||
pareto_front,
|
||||
best,
|
||||
objectives,
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,45 @@
|
||||
//! Per-generation observation payload passed to [`Observer`](super::Observer).
|
||||
|
||||
use std::time::Duration;
|
||||
|
||||
use crate::core::candidate::Candidate;
|
||||
use crate::core::objective::ObjectiveSpace;
|
||||
|
||||
/// A view of an optimizer's state at one generation boundary.
|
||||
///
|
||||
/// Borrowed (`&'a ...`) rather than owned so the algorithm doesn't
|
||||
/// have to clone the whole population on every call. Observers that
|
||||
/// need to retain values across calls should clone what they need
|
||||
/// out of the snapshot.
|
||||
#[derive(Debug)]
|
||||
pub struct Snapshot<'a, D> {
|
||||
/// Zero-indexed generation count. The first call is `iteration = 0`
|
||||
/// for "after the initial population was built and evaluated";
|
||||
/// subsequent calls are after generation 1, 2, …
|
||||
pub iteration: usize,
|
||||
|
||||
/// Total `Problem::evaluate` calls so far, including the initial
|
||||
/// population.
|
||||
pub evaluations: usize,
|
||||
|
||||
/// Wall-clock time since `run_with` started.
|
||||
pub elapsed: Duration,
|
||||
|
||||
/// The current population (whatever the algorithm considers the
|
||||
/// "live" set this generation). For steady-state algorithms this
|
||||
/// is the post-replacement population.
|
||||
pub population: &'a [Candidate<D>],
|
||||
|
||||
/// The current Pareto front, if the algorithm tracks one. `None`
|
||||
/// for single-objective algorithms.
|
||||
pub pareto_front: Option<&'a [Candidate<D>]>,
|
||||
|
||||
/// The current best candidate. `Some` for single-objective
|
||||
/// algorithms; `None` for multi-objective unless the algorithm
|
||||
/// tracks a notion of best (some don't).
|
||||
pub best: Option<&'a Candidate<D>>,
|
||||
|
||||
/// The objective space, useful for observers that need to convert
|
||||
/// raw objective values to minimization-oriented form.
|
||||
pub objectives: &'a ObjectiveSpace,
|
||||
}
|
||||
@@ -11,6 +11,14 @@ pub use crate::core::{
|
||||
|
||||
pub use crate::traits::{Initializer, Optimizer, Repair, Variation};
|
||||
|
||||
#[cfg(feature = "tracing")]
|
||||
pub use crate::observer::builtin::TracingObserver;
|
||||
pub use crate::observer::{
|
||||
Observer, Snapshot,
|
||||
builtin::{AllOf, AnyOf, MaxIterations, MaxTime, Periodic, Stagnation, TargetFitness},
|
||||
};
|
||||
pub use std::ops::ControlFlow;
|
||||
|
||||
pub use crate::pareto::{
|
||||
Dominance, ParetoArchive, best_candidate, crowding_distance, das_dennis, non_dominated_sort,
|
||||
pareto_compare, pareto_front,
|
||||
|
||||
+56
-3
@@ -1,18 +1,71 @@
|
||||
//! The single trait users implement to add a new optimizer.
|
||||
|
||||
use std::time::{Duration, Instant};
|
||||
|
||||
use crate::core::problem::Problem;
|
||||
use crate::core::result::OptimizationResult;
|
||||
use crate::observer::{Observer, Snapshot};
|
||||
|
||||
/// An optimizer that runs to completion in a single call.
|
||||
///
|
||||
/// Implementations own their main loop, manage their own state, and return an
|
||||
/// [`OptimizationResult`]. v1 deliberately does not expose a step-by-step API
|
||||
/// or an associated error type — invalid configuration may panic with a clear
|
||||
/// message.
|
||||
/// [`OptimizationResult`]. Invalid configuration panics with a clear
|
||||
/// message rather than returning a `Result`.
|
||||
pub trait Optimizer<P>
|
||||
where
|
||||
P: Problem,
|
||||
{
|
||||
/// Run the optimizer to completion against `problem`.
|
||||
fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision>;
|
||||
|
||||
/// Run with an [`Observer`] called after each generation.
|
||||
///
|
||||
/// The observer can halt the run by returning
|
||||
/// [`std::ops::ControlFlow::Break`]; the partial result is still
|
||||
/// returned. Built-in observers in
|
||||
/// [`heuropt::observer::builtin`](crate::observer::builtin) cover
|
||||
/// the common stop conditions (`MaxTime`, `TargetFitness`,
|
||||
/// `Stagnation`, …).
|
||||
///
|
||||
/// **Default impl:** falls back to `run` plus a single final
|
||||
/// notification. Algorithms that override this method get true
|
||||
/// per-generation observation; algorithms that don't get a single
|
||||
/// notification at the end. The trait-level docstring on each
|
||||
/// algorithm calls out which behavior it supports.
|
||||
fn run_with<O>(&mut self, problem: &P, observer: &mut O) -> OptimizationResult<P::Decision>
|
||||
where
|
||||
O: Observer<P::Decision>,
|
||||
{
|
||||
let started = Instant::now();
|
||||
let result = self.run(problem);
|
||||
let elapsed = started.elapsed();
|
||||
notify_final(&result, elapsed, problem, observer);
|
||||
result
|
||||
}
|
||||
}
|
||||
|
||||
/// Helper used by the default `run_with` impl: build a single final-
|
||||
/// state snapshot and hand it to the observer once. Algorithms that
|
||||
/// override `run_with` for per-generation reporting don't go through
|
||||
/// this path — they construct their own per-iteration snapshots.
|
||||
fn notify_final<P, O>(
|
||||
result: &OptimizationResult<P::Decision>,
|
||||
elapsed: Duration,
|
||||
problem: &P,
|
||||
observer: &mut O,
|
||||
) where
|
||||
P: Problem,
|
||||
O: Observer<P::Decision>,
|
||||
{
|
||||
let objectives = problem.objectives();
|
||||
let snap = Snapshot {
|
||||
iteration: result.generations,
|
||||
evaluations: result.evaluations,
|
||||
elapsed,
|
||||
population: result.population.as_slice(),
|
||||
pareto_front: Some(result.pareto_front.as_slice()),
|
||||
best: result.best.as_ref(),
|
||||
objectives: &objectives,
|
||||
};
|
||||
let _ = observer.observe(&snap);
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user