heuropt/CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Unreleased

Added

New algorithms (the "expensive-eval and gradient-free" cohort)

• OnePlusOneEs — Rechenberg 1973 (1+1)-ES with the one-fifth success rule. Smallest possible self-adapting evolution strategy.
• NelderMead — Nelder & Mead 1965 simplex direct-search method. Fills a real gap: heuropt's first classical gradient-free local optimizer.
• IpopCmaEs — Auger & Hansen 2005 increasing-population CMA-ES with restart. Specifically fixes vanilla CMA-ES's known weakness on multimodal problems (e.g. Rastrigin: 2.35 → 0.13).
• BayesianOpt — Gaussian-process-based Bayesian Optimization with Expected Improvement acquisition. heuropt's first sample-efficient algorithm: targets the 50–500 evaluation regime where every other algorithm is way over-budget.

Internal helpers

• internal::cholesky — Cholesky factorization + triangular solves for symmetric positive-definite matrices, used by the GP posterior in BayesianOpt. Hand-rolled to avoid pulling in nalgebra.

CmaEs API change (additive)

• CmaEsConfig gained an initial_mean: Option<Vec<f64>> field (defaulting to None, which keeps the existing midpoint-of-bounds behavior). IpopCmaEs uses it to inject restart diversity without shrinking the search box.

0.2.0 — 2026-05-05

A substantial expansion of the algorithm catalog (21 new algorithms), five new operators, an n-D hypervolume utility, an algorithm-selection guide in the README, and a multi-seed comparison harness covering seven benchmark problems. No breaking changes to the v0.1.0 public API.

Added

New algorithms

Single-objective:

• HillClimber — simplest greedy local search.
• SimulatedAnnealing — Kirkpatrick et al. 1983, generic over decision type.
• GeneticAlgorithm — generational SO GA with tournament selection + elitism.
• ParticleSwarm — Eberhart & Kennedy 1995 PSO for Vec<f64>.
• CmaEs — Hansen & Ostermeier 2001 covariance-matrix adaptation.
• TabuSearch — Glover 1986, with a user-supplied neighbor generator.
• AntColonyTsp — Dorigo Ant System for permutation problems.
• Umda — Mühlenbein 1997 univariate marginal-distribution EDA for Vec<bool>.
• Tlbo — Rao 2011 Teaching-Learning-Based Optimization (parameter-free).

Multi-objective:

• Mopso — Coello, Pulido & Lechuga 2004 multi-objective PSO.
• Ibea — Zitzler & Künzli 2004 indicator-based EA.
• SmsEmoa — Beume, Naujoks & Emmerich 2007 S-metric selection EMOA.
• Hype — Bader & Zitzler 2011 Hypervolume Estimation Algorithm.
• Rvea — Cheng et al. 2016 Reference Vector-guided EA.
• PesaII — Corne et al. 2001 Pareto Envelope-based Selection II.
• EpsilonMoea — Deb, Mohan & Mishra 2003 ε-dominance MOEA.
• AgeMoea — Panichella 2019 Adaptive Geometry Estimation MOEA.
• Grea — Yang et al. 2013 Grid-based EA.
• Knea — Zhang, Tian & Jin 2015 Knee point-driven EA.

New operators

• BoundedGaussianMutation — Gaussian noise + per-axis clamping.
• SimulatedBinaryCrossover (SBX) — Deb & Agrawal 1995 canonical real-valued crossover.
• PolynomialMutation — Deb's polynomial mutation, the standard NSGA-II pair to SBX.
• CompositeVariation — pipeline two Variation operators (typically crossover → mutation).
• LevyMutation — heavy-tailed Lévy-flight mutation via Mantegna's algorithm.

New metrics / utilities

• hypervolume_nd — exact N-dimensional dominated hypervolume via the Hypervolume-by-Slicing-Objectives (HSO) algorithm, plus an internal Jacobi symmetric eigendecomposition helper used by CMA-ES.

New examples

• compare — multi-seed comparison harness running every applicable algorithm across ZDT1, ZDT3, DTLZ1, DTLZ2 (multi/many-objective) and Rastrigin, Rosenbrock, Ackley (single-objective). Reports hypervolume, spacing, mean L2/dist, front size, and wall-clock ms.
• benchmarks — canonical reference runs of NSGA-II on ZDT1 and DE on Rastrigin.
• jiggly_tuning — real-world 4-objective NSGA-III firmware tuning for the jiggly USB-mouse-jiggler, with an a-posteriori weighted-decision step that picks one recommendation off the Pareto front.

New optional feature

• parallel — rayon-backed parallel population evaluation in RandomSearch, Nsga2, DifferentialEvolution, Spea2, Ibea, Mopso, and most other algorithms with batchable inner loops. Seeded runs stay bit-identical to serial mode.

Documentation

• README gained an explanatory algorithm-selection decision tree that walks newcomers through choosing an optimizer, defining the terminology (multi-objective, Pareto front, dominance, multimodality, evaluation cost) as it goes.

Changed

• Minimum supported Rust version remains 1.85 (edition 2024).
• Algorithm impls now require P: Sync and P::Decision: Send so the same impl serves both parallel and serial feature builds. Any Problem / decision type without exotic interior mutability already satisfies these.

0.1.0 — 2026-05-04

Initial release.

Core types and traits

• Direction, Objective, ObjectiveSpace (with as_minimization direction conversion).
• Evaluation with feasibility (constraint_violation <= 0.0).
• Candidate<D>, Population<D>, OptimizationResult<D>.
• type Rng = rand::rngs::StdRng and rng_from_seed so no public trait is generic over the RNG.
• Problem, Optimizer<P>, Initializer<D>, Variation<D>.

Pareto utilities

• pareto_compare, pareto_front, best_candidate, non_dominated_sort (Deb fast non-dominated sort), crowding_distance, ParetoArchive<D>, das_dennis (structured reference points for NSGA-III and MOEA/D).

Operators

• Real: RealBounds, GaussianMutation, BoundedGaussianMutation, SimulatedBinaryCrossover (SBX), PolynomialMutation.
• Binary: BitFlipMutation.
• Permutation: SwapMutation.
• CompositeVariation pipeline (typically crossover → mutation).

Selection helpers

• select_random, tournament_select_single_objective.

Reference algorithms

• RandomSearch — sample-evaluate-keep baseline.
• Paes — small (1+1) Pareto Archived Evolution Strategy.
• Nsga2 — canonical Pareto-based EA with crowding distance.
• Nsga3 — many-objective NSGA-III with reference-point niching.
• Spea2 — Strength Pareto Evolutionary Algorithm 2.
• Moead — decomposition-based MOEA/D with the Tchebycheff scalar.
• DifferentialEvolution — single-objective DE/rand/1/bin.

Metrics

• spacing (Schott), hypervolume_2d (exact 2-D dominated hypervolume).

Examples

• random_search, toy_nsga2, custom_optimizer — minimum-viable walkthroughs.
• benchmarks — ZDT1 and Rastrigin reference runs.
• compare — multi-seed comparison harness running every applicable algorithm on ZDT1 (2-obj), DTLZ2 (3-obj), and Rastrigin (single-obj), reporting hypervolume, spacing, mean L2, front size, and wall time.
• jiggly_tuning — 4-objective NSGA-III tuning of the jiggly USB-mouse-jiggler firmware with an a-posteriori weighted-decision step that picks one recommendation off the Pareto front.

Optional features

• serde — Serialize / Deserialize derives on the core data types.
• parallel — rayon-backed parallel population evaluation in RandomSearch, Nsga2, and DifferentialEvolution. Seeded runs stay bit-identical to serial mode.