Async coverage was incomplete in 0.7 (only RandomSearch and
DifferentialEvolution had run_async). 0.8 closes the gap: every one
of the 33 algorithms now exposes
run_async(&problem, concurrency).await, gated on the async feature.
- Population-based algorithms fan out per-generation evaluations
through evaluate_batch_async with concurrency-bounded
FuturesOrdered chunks.
- Steady-state algorithms (HillClimber, SimulatedAnnealing,
OnePlusOneEs, Paes, NelderMead) await each step sequentially;
they accept the concurrency parameter for API uniformity.
- TabuSearch fans out the K-neighbor batch each step.
- Surrogate algorithms (BayesianOpt, Tpe) batch the initial design
and await per-iteration acquisitions sequentially so the surrogate
can update between picks.
- Hyperband uses a new AsyncPartialProblem trait (mirroring
PartialProblem for multi-fidelity workloads) and a parallel
evaluate_batch_at_budget_async helper; each Successive-Halving
rung fans out its budgeted evaluations.
All paths preserve seeded determinism: RNG draws happen on the main
task in the same order as the sync path, and only the evaluations
are concurrent.
Adds a dedicated cookbook recipe at docs/book/src/cookbook/async.md
with a worked example (DifferentialEvolution under tokio) and
guidance on picking concurrency. Cross-references in SUMMARY.md
and cookbook.md are updated to surface the new recipe.
The follow-up docs commit reconciles the rest of the user guide
and README to describe the new feature; this commit is the bare
async surface.
Theme: documentation and project polish. No public-API changes; this
is the v0.5 release that elevates heuropt's docs/onboarding/governance
to bar-setting status.
Adds:
- mdbook user guide at docs/book/ with intro, getting-started,
defining-problems, choosing-an-algorithm, cookbook (7 recipes),
comparison vs other libraries, stability/SemVer, migration guides.
Deploys to https://swaits.github.io/heuropt/ via .github/workflows/
docs.yml.
- Runnable rustdoc examples on every algorithm (35 of them), all
exercised by cargo test --doc.
- Three real-world examples: portfolio.rs (multi-obj with budget
constraint), hyperparam_tuning.rs (BO + TPE), scheduling.rs
(permutation via SA + SwapMutation against Smith's-rule oracle).
- Governance: CONTRIBUTING.md, SECURITY.md, CODE_OF_CONDUCT.md
(adopting builderscode.org's Builder's Code of Conduct), GitHub
issue templates, PR template.
Polishes:
- README hero with badges + user-guide link.
- lib.rs crate-level docs.
- CHANGELOG entry for 0.5.0.
Bumps Cargo.toml to 0.5.0.
Bader & Zitzler 2011: HypE estimates hypervolume contributions via
Monte Carlo sampling instead of computing them exactly. The point of
the trick is that exact hypervolume becomes prohibitively expensive
beyond ~5 objectives, while MC sampling stays cheap and accurate
enough at any dimension.
Each generation:
- Generate offspring via parent selection + variation + evaluation
- Combine, run non_dominated_sort, fill front-by-front
- For the splitting front, estimate each member's HV contribution
by drawing `n_samples` uniform points in the box [ideal, reference]
and counting how many points are dominated by *exactly* one front
member — that count, divided by n_samples and multiplied by the
box volume, is the member's expected unique HV contribution.
- Drop members one at a time from the splitting front by smallest
estimated contribution.
Public API matches the rest of the MO algorithms (Config + Optimizer).
The reference point is supplied in the config so the user controls
the integration domain. Tests cover non-empty front, deterministic
reruns, and panic on dim-mismatched reference.
swaits