moors 0.2.10

Solving multi-objective optimization problems using genetic algorithms.
Documentation 
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//! # NSGA‑III – Reference‑Point‑Based Many‑Objective GA
//!
//! Implementation of
//! **K. Deb & H. Jain,
//! “An Evolutionary Many‑Objective Optimization Algorithm Using
//! Reference‑Point‑Based Nondominated Sorting Approach, Part I:
//! Solving Problems with Box Constraints”,
//! IEEE Transactions on Evolutionary Computation 18 (4): 577‑601 (2014).**
//!
//! NSGA‑III extends NSGA‑II to many‑objective problems (≥ 3 objectives) by
//! replacing crowding‑distance with a *reference‑point association* that drives
//! the population towards a well‑spread Pareto front.
//!
//! In *moors*, NSGA‑III is wired from reusable operator bricks:
//!
//! * **Selection:** [`RandomSelection`] (uniform binary tournament)
//! * **Survival:**  [`Nsga3ReferencePointsSurvival`] (rank + reference‑point niching)
//! * **Crossover / Mutation / Sampling:** user‑provided via the builder.

use ndarray::Array2;

use crate::{
    define_algorithm_and_builder,
    operators::{
        selection::moo::Nsga3RandomSelection, survival::moo::Nsga3ReferencePointsSurvival,
    },
};

define_algorithm_and_builder!(
    /// NSGA-III algorithm wrapper.
    ///
    /// This struct is a thin facade over [`GeneticAlgorithm`] preset with
    /// the NSGA-III survival and selection strategy.
    ///
    /// * **Selection:** [`RandomSelection`]
    /// * **Survival:**  [`Nsga3ReferencePointsSurvival`] (elitist, reference-point based)
    ///
    /// Construct it with [`Nsga3Builder`](crate::algorithms::Nsga3Builder).
    /// After building, call [`run`](GeneticAlgorithm::run)
    /// and then [`population`](GeneticAlgorithm::population) to retrieve the
    /// final non-dominated set.
    ///
    /// For algorithmic details, see:
    /// Kalyanmoy Deb and Himanshu Jain (2014),
    /// "An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based
    /// Nondominated Sorting Approach, Part I: Solving Problems with Box Constraints",
    /// *IEEE Transactions on Evolutionary Computation*, vol. 18, no. 4,
    /// pp. 577–601, Aug. 2014.
    /// DOI: 10.1109/TEVC.2013.2281535
    Nsga3,
    Nsga3RandomSelection,
    Nsga3ReferencePointsSurvival,
    survival_args = [ reference_points: Array2<f64>, are_aspirational: bool ]
);