swaits
9d46cf9d65
Implementation of Zitzler, Laumanns, Thiele 2001 SPEA2 — the classic
Pareto MOEA built around an explicit external archive of fixed size.
Each generation:
1. Combine the current population and the archive into one pool.
2. For every member, compute strength S(i) = number of others that
member dominates, then raw fitness R(i) = sum of S(j) over members
j that dominate i.
3. Add a density estimator D(i) = 1/(σ_k + 2) where σ_k is the distance
to the k-th nearest neighbor (k = floor(sqrt(|pool|))) in
minimization-oriented objective space.
4. Final fitness F(i) = R(i) + D(i); lower is better.
5. Build the next archive by taking every non-dominated member
(R(i) == 0). If too many, prune by repeatedly removing the member
with the smallest k-th-nearest-neighbor distance. If too few, fill
from the rest sorted by F ascending.
6. Generate the next population by binary tournament on F (lower wins),
then variation, then evaluation.
Public API mirrors the other algorithms:
Spea2Config { population_size, archive_size, generations, seed }
Spea2 { config, initializer, variation }
impl<P, I, V> Optimizer<P> for Spea2<I, V>
Re-exported from the prelude. Tests cover archive size invariants,
non-empty Pareto front on Schaffer N.1, deterministic reruns under
the same seed, and panic on population_size == 0.
15 lines
280 B
Rust
15 lines
280 B
Rust
//! Built-in reference optimizers.
|
|
|
|
pub mod differential_evolution;
|
|
pub mod nsga2;
|
|
pub mod paes;
|
|
pub(crate) mod parallel_eval;
|
|
pub mod random_search;
|
|
pub mod spea2;
|
|
|
|
pub use differential_evolution::*;
|
|
pub use nsga2::*;
|
|
pub use paes::*;
|
|
pub use random_search::*;
|
|
pub use spea2::*;
|