swaits
cbfedd85fa
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.
542 lines
18 KiB
Rust
542 lines
18 KiB
Rust
//! `Hype` — Bader & Zitzler 2011 Hypervolume Estimation Algorithm.
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//!
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//! HypE replaces the exact hypervolume contribution used in SMS-EMOA with
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//! a Monte Carlo estimate, so it scales to arbitrary objective counts at
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//! the cost of stochastic noise on the contribution estimate.
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use rand::Rng as _;
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use crate::algorithms::parallel_eval::evaluate_batch;
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use crate::core::candidate::Candidate;
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use crate::core::objective::ObjectiveSpace;
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use crate::core::population::Population;
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use crate::core::problem::Problem;
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use crate::core::result::OptimizationResult;
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use crate::core::rng::{Rng, rng_from_seed};
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use crate::pareto::front::{best_candidate, pareto_front};
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use crate::pareto::sort::non_dominated_sort;
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use crate::traits::{Initializer, Optimizer, Variation};
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/// Configuration for [`Hype`].
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#[derive(Debug, Clone)]
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pub struct HypeConfig {
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/// Constant population size.
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pub population_size: usize,
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/// Number of generations.
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pub generations: usize,
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/// Reference point used to bound the Monte Carlo integration box.
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/// Must have one entry per objective; should be worse than every
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/// realistic objective value.
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pub reference_point: Vec<f64>,
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/// Number of Monte Carlo samples per HV estimation step.
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pub mc_samples: usize,
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/// Seed for the deterministic RNG.
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pub seed: u64,
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}
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impl Default for HypeConfig {
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fn default() -> Self {
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Self {
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population_size: 100,
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generations: 250,
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reference_point: vec![11.0, 11.0],
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mc_samples: 10_000,
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seed: 42,
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}
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}
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}
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/// Hypervolume Estimation Algorithm: many-objective MOEA that selects via
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/// Monte Carlo–estimated hypervolume contributions.
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///
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/// # Example
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///
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/// ```
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/// use heuropt::prelude::*;
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///
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/// struct Schaffer;
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/// impl Problem for Schaffer {
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/// type Decision = Vec<f64>;
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/// fn objectives(&self) -> ObjectiveSpace {
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/// ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
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/// }
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/// fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
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/// Evaluation::new(vec![x[0] * x[0], (x[0] - 2.0).powi(2)])
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/// }
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/// }
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///
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/// let bounds = vec![(-5.0_f64, 5.0_f64)];
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/// let mut opt = Hype::new(
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/// HypeConfig {
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/// population_size: 20,
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/// generations: 20,
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/// reference_point: vec![30.0, 30.0],
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/// mc_samples: 100,
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/// seed: 42,
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/// },
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/// RealBounds::new(bounds.clone()),
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/// CompositeVariation {
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/// crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
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/// mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
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/// },
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/// );
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/// let r = opt.run(&Schaffer);
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/// assert!(!r.pareto_front.is_empty());
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/// ```
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#[derive(Debug, Clone)]
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pub struct Hype<I, V> {
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/// Algorithm configuration.
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pub config: HypeConfig,
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/// Initial-decision sampler.
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pub initializer: I,
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/// Offspring-producing variation operator.
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pub variation: V,
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}
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impl<I, V> Hype<I, V> {
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/// Construct a `Hype`.
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pub fn new(config: HypeConfig, initializer: I, variation: V) -> Self {
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Self {
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config,
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initializer,
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variation,
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}
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}
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}
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impl<P, I, V> Optimizer<P> for Hype<I, V>
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where
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P: Problem + Sync,
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P::Decision: Send,
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I: Initializer<P::Decision>,
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V: Variation<P::Decision>,
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{
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fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision> {
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assert!(
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self.config.population_size > 0,
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"Hype population_size must be > 0"
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);
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assert!(self.config.mc_samples > 0, "Hype mc_samples must be > 0");
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let n = self.config.population_size;
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let objectives = problem.objectives();
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assert_eq!(
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self.config.reference_point.len(),
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objectives.len(),
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"Hype reference_point.len() must equal number of objectives",
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);
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let reference = self.config.reference_point.clone();
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let mut rng = rng_from_seed(self.config.seed);
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let initial_decisions = self.initializer.initialize(n, &mut rng);
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let mut population: Vec<Candidate<P::Decision>> =
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evaluate_batch(problem, initial_decisions);
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let mut evaluations = population.len();
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for _ in 0..self.config.generations {
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// Phase 1: parent selection + variation (random tournament on
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// a fitness-by-HV-estimate proxy).
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let fitness = hype_fitness(
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&population,
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&objectives,
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&reference,
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self.config.mc_samples,
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&mut rng,
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);
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let mut offspring_decisions: Vec<P::Decision> = Vec::with_capacity(n);
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while offspring_decisions.len() < n {
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let p1 = binary_tournament(&fitness, &mut rng);
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let p2 = binary_tournament(&fitness, &mut rng);
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let parents = vec![
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population[p1].decision.clone(),
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population[p2].decision.clone(),
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];
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let children = self.variation.vary(&parents, &mut rng);
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assert!(!children.is_empty(), "Hype variation returned no children");
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for child in children {
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if offspring_decisions.len() >= n {
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break;
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}
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offspring_decisions.push(child);
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}
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}
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// Phase 2: parallel-friendly batch evaluation.
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let offspring = evaluate_batch(problem, offspring_decisions);
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evaluations += offspring.len();
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// Phase 3: combine + survival via front-by-front fill plus
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// estimated-contribution truncation on the splitting front.
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let mut combined: Vec<Candidate<P::Decision>> = Vec::with_capacity(2 * n);
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combined.extend(population);
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combined.extend(offspring);
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let fronts = non_dominated_sort(&combined, &objectives);
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let mut keep_indices: Vec<usize> = Vec::with_capacity(n);
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let mut splitting: &[usize] = &[];
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for f in &fronts {
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if keep_indices.len() + f.len() <= n {
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keep_indices.extend(f.iter().copied());
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} else {
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splitting = f;
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break;
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}
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if keep_indices.len() == n {
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break;
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}
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}
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if keep_indices.len() < n {
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// Need to choose `n - keep_indices.len()` from `splitting`
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// by largest HV contribution.
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let pool: Vec<&Candidate<P::Decision>> =
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splitting.iter().map(|&i| &combined[i]).collect();
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let contributions = estimate_contributions(
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&pool,
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&objectives,
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&reference,
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self.config.mc_samples,
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&mut rng,
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);
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let mut order: Vec<usize> = (0..splitting.len()).collect();
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order.sort_by(|&a, &b| {
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contributions[b]
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.partial_cmp(&contributions[a])
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.unwrap_or(std::cmp::Ordering::Equal)
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});
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for k in order.into_iter().take(n - keep_indices.len()) {
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keep_indices.push(splitting[k]);
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}
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}
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// Materialize the next generation.
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population = keep_indices
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.into_iter()
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.map(|i| combined[i].clone())
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.collect();
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}
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let front = pareto_front(&population, &objectives);
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let best = best_candidate(&population, &objectives);
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OptimizationResult::new(
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Population::new(population),
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front,
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best,
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evaluations,
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self.config.generations,
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)
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}
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}
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#[cfg(feature = "async")]
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impl<I, V> Hype<I, V> {
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/// Async version of [`Optimizer::run`] — drives evaluations through
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/// the user-chosen async runtime. Available only with the `async`
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/// feature.
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///
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/// `concurrency` bounds in-flight evaluations per batch.
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pub async fn run_async<P>(
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&mut self,
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problem: &P,
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concurrency: usize,
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) -> OptimizationResult<P::Decision>
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where
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P: crate::core::async_problem::AsyncProblem,
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I: Initializer<P::Decision>,
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V: Variation<P::Decision>,
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{
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use crate::algorithms::parallel_eval_async::evaluate_batch_async;
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assert!(
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self.config.population_size > 0,
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"Hype population_size must be > 0"
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);
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assert!(self.config.mc_samples > 0, "Hype mc_samples must be > 0");
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let n = self.config.population_size;
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let objectives = problem.objectives();
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assert_eq!(
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self.config.reference_point.len(),
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objectives.len(),
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"Hype reference_point.len() must equal number of objectives",
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);
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let reference = self.config.reference_point.clone();
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let mut rng = rng_from_seed(self.config.seed);
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let initial_decisions = self.initializer.initialize(n, &mut rng);
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let mut population: Vec<Candidate<P::Decision>> =
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evaluate_batch_async(problem, initial_decisions, concurrency).await;
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let mut evaluations = population.len();
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for _ in 0..self.config.generations {
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let fitness = hype_fitness(
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&population,
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&objectives,
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&reference,
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self.config.mc_samples,
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&mut rng,
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);
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let mut offspring_decisions: Vec<P::Decision> = Vec::with_capacity(n);
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while offspring_decisions.len() < n {
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let p1 = binary_tournament(&fitness, &mut rng);
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let p2 = binary_tournament(&fitness, &mut rng);
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let parents = vec![
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population[p1].decision.clone(),
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population[p2].decision.clone(),
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];
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let children = self.variation.vary(&parents, &mut rng);
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assert!(!children.is_empty(), "Hype variation returned no children");
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for child in children {
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if offspring_decisions.len() >= n {
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break;
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}
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offspring_decisions.push(child);
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}
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}
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let offspring = evaluate_batch_async(problem, offspring_decisions, concurrency).await;
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evaluations += offspring.len();
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let mut combined: Vec<Candidate<P::Decision>> = Vec::with_capacity(2 * n);
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combined.extend(population);
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combined.extend(offspring);
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let fronts = non_dominated_sort(&combined, &objectives);
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let mut keep_indices: Vec<usize> = Vec::with_capacity(n);
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let mut splitting: &[usize] = &[];
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for f in &fronts {
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if keep_indices.len() + f.len() <= n {
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keep_indices.extend(f.iter().copied());
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} else {
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splitting = f;
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break;
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}
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if keep_indices.len() == n {
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break;
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}
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}
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if keep_indices.len() < n {
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let pool: Vec<&Candidate<P::Decision>> =
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splitting.iter().map(|&i| &combined[i]).collect();
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let contributions = estimate_contributions(
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&pool,
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&objectives,
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&reference,
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self.config.mc_samples,
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&mut rng,
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);
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let mut order: Vec<usize> = (0..splitting.len()).collect();
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order.sort_by(|&a, &b| {
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contributions[b]
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.partial_cmp(&contributions[a])
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.unwrap_or(std::cmp::Ordering::Equal)
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});
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for k in order.into_iter().take(n - keep_indices.len()) {
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keep_indices.push(splitting[k]);
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}
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}
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population = keep_indices
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.into_iter()
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.map(|i| combined[i].clone())
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.collect();
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}
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let front = pareto_front(&population, &objectives);
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let best = best_candidate(&population, &objectives);
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OptimizationResult::new(
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Population::new(population),
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front,
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best,
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evaluations,
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self.config.generations,
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)
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}
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}
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fn hype_fitness<D>(
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pool: &[Candidate<D>],
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objectives: &ObjectiveSpace,
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reference: &[f64],
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samples: usize,
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rng: &mut Rng,
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) -> Vec<f64> {
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if pool.is_empty() {
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return Vec::new();
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}
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let pool_refs: Vec<&Candidate<D>> = pool.iter().collect();
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estimate_contributions(&pool_refs, objectives, reference, samples, rng)
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}
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/// Estimate each candidate's expected unique hypervolume contribution by
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/// Monte Carlo sampling uniformly inside the [ideal, reference] box and
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/// counting per-sample which candidates dominate it. A sample dominated
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/// by exactly one candidate contributes 1/samples × box_volume to that
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/// candidate; samples dominated by k candidates contribute proportionally
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/// less, weighted by HypE's "weighted hypervolume" rule (1 / k).
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fn estimate_contributions<D>(
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pool: &[&Candidate<D>],
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objectives: &ObjectiveSpace,
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reference: &[f64],
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samples: usize,
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rng: &mut Rng,
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) -> Vec<f64> {
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let n = pool.len();
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if n == 0 {
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return Vec::new();
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}
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let m = reference.len();
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// Cache minimization-oriented objective values.
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let oriented: Vec<Vec<f64>> = pool
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.iter()
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.map(|c| objectives.as_minimization(&c.evaluation.objectives))
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.collect();
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// Compute the lower bound (ideal) of the integration box: per-axis min
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// across the population, capped at the reference (so the box has
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// non-negative width even if no point dominates the reference).
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let mut lower = vec![f64::INFINITY; m];
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for o in &oriented {
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for (k, &v) in o.iter().enumerate() {
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if v < lower[k] {
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lower[k] = v;
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}
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}
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}
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for k in 0..m {
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if !lower[k].is_finite() || lower[k] >= reference[k] {
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// No point on this axis dominates the reference → zero
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// contribution everywhere.
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return vec![0.0; n];
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}
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}
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let box_volume: f64 = (0..m).map(|k| reference[k] - lower[k]).product();
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if box_volume <= 0.0 {
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return vec![0.0; n];
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}
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let mut contrib = vec![0.0_f64; n];
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let mut sample = vec![0.0_f64; m];
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for _ in 0..samples {
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for k in 0..m {
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let u: f64 = rng.random();
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sample[k] = lower[k] + u * (reference[k] - lower[k]);
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}
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// Count and identify candidates that dominate this sample (point
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// in the box).
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let mut dominators: Vec<usize> = Vec::with_capacity(n);
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for (i, o) in oriented.iter().enumerate() {
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if o.iter().zip(sample.iter()).all(|(p, s)| *p <= *s) {
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dominators.push(i);
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}
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}
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if dominators.is_empty() {
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continue;
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}
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// HypE weighting: each sample contributes 1/k to each of its k
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// dominators. (This generalizes "exactly-one dominator" to
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// arbitrary multiplicities.)
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let weight = 1.0 / dominators.len() as f64;
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for i in dominators {
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contrib[i] += weight;
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}
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}
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let scale = box_volume / samples as f64;
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contrib.into_iter().map(|c| c * scale).collect()
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}
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fn binary_tournament(fitness: &[f64], rng: &mut Rng) -> usize {
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let a = rng.random_range(0..fitness.len());
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let b = rng.random_range(0..fitness.len());
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if fitness[a] > fitness[b] {
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a
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} else if fitness[a] < fitness[b] {
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b
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} else if rng.random_bool(0.5) {
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a
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} else {
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b
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::operators::{
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CompositeVariation, PolynomialMutation, RealBounds, SimulatedBinaryCrossover,
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};
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use crate::tests_support::SchafferN1;
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fn make_optimizer(
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seed: u64,
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) -> Hype<RealBounds, CompositeVariation<SimulatedBinaryCrossover, PolynomialMutation>> {
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let bounds = vec![(-5.0, 5.0)];
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let initializer = RealBounds::new(bounds.clone());
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let variation = CompositeVariation {
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crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
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mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
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};
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Hype::new(
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HypeConfig {
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population_size: 20,
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generations: 15,
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reference_point: vec![30.0, 30.0],
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mc_samples: 1_000,
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seed,
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},
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initializer,
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variation,
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)
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}
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#[test]
|
||
fn produces_pareto_front() {
|
||
let mut opt = make_optimizer(1);
|
||
let r = opt.run(&SchafferN1);
|
||
assert_eq!(r.population.len(), 20);
|
||
assert!(!r.pareto_front.is_empty());
|
||
}
|
||
|
||
#[test]
|
||
fn deterministic_with_same_seed() {
|
||
let mut a = make_optimizer(99);
|
||
let mut b = make_optimizer(99);
|
||
let ra = a.run(&SchafferN1);
|
||
let rb = b.run(&SchafferN1);
|
||
let oa: Vec<Vec<f64>> = ra
|
||
.pareto_front
|
||
.iter()
|
||
.map(|c| c.evaluation.objectives.clone())
|
||
.collect();
|
||
let ob: Vec<Vec<f64>> = rb
|
||
.pareto_front
|
||
.iter()
|
||
.map(|c| c.evaluation.objectives.clone())
|
||
.collect();
|
||
assert_eq!(oa, ob);
|
||
}
|
||
|
||
#[test]
|
||
#[should_panic(expected = "reference_point.len() must equal number of objectives")]
|
||
fn dim_mismatch_panics() {
|
||
let bounds = vec![(0.0, 1.0)];
|
||
let initializer = RealBounds::new(bounds.clone());
|
||
let variation = CompositeVariation {
|
||
crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
|
||
mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
|
||
};
|
||
let mut opt = Hype::new(
|
||
HypeConfig {
|
||
population_size: 4,
|
||
generations: 1,
|
||
reference_point: vec![1.0, 1.0, 1.0],
|
||
mc_samples: 100,
|
||
seed: 0,
|
||
},
|
||
initializer,
|
||
variation,
|
||
);
|
||
let _ = opt.run(&SchafferN1);
|
||
}
|
||
}
|