feat(algorithms): add IBEA (Indicator-Based Evolutionary Algorithm)

Zitzler & Künzli 2004 IBEA: replaces Pareto-rank + crowding fitness
with a single scalar fitness derived from a binary quality indicator
(here, the additive ε-indicator). Loses no information at three or
more objectives the way crowding distance does.

Algorithm:
- For every (i, j) pair compute I(i, j) = max_k (f_k(i) - f_k(j)) on
  minimization-oriented objectives.
- Fitness F(i) = -Σ_{j≠i} exp(-I(j, i) / κ).
- Each generation: combine parents + offspring, iteratively remove the
  lowest-F member (cleanly recomputing the contribution of the dropped
  member from each surviving member's fitness) until population_size
  remain.
- Parent selection: binary tournament on F (higher wins).

Bounds-aware operators recommended (SBX + PolyMut).
Tests: produces a non-empty front on Schaffer N.1, deterministic
reruns, panic on `population_size == 0`.

src/algorithms/ibea.rs

@@ -0,0 +1,330 @@
+//! `Ibea` — Zitzler & Künzli 2004 Indicator-Based Evolutionary Algorithm.
+
+use rand::Rng as _;
+
+use crate::algorithms::parallel_eval::evaluate_batch;
+use crate::core::candidate::Candidate;
+use crate::core::objective::ObjectiveSpace;
+use crate::core::population::Population;
+use crate::core::problem::Problem;
+use crate::core::result::OptimizationResult;
+use crate::core::rng::{Rng, rng_from_seed};
+use crate::pareto::front::{best_candidate, pareto_front};
+use crate::traits::{Initializer, Optimizer, Variation};
+
+/// Configuration for [`Ibea`].
+#[derive(Debug, Clone)]
+pub struct IbeaConfig {
+    /// Constant population size carried across generations.
+    pub population_size: usize,
+    /// Number of generations.
+    pub generations: usize,
+    /// Indicator scaling factor `κ`. Default 0.05 (Zitzler & Künzli §3.2).
+    pub kappa: f64,
+    /// Seed for the deterministic RNG.
+    pub seed: u64,
+}
+
+impl Default for IbeaConfig {
+    fn default() -> Self {
+        Self { population_size: 100, generations: 250, kappa: 0.05, seed: 42 }
+    }
+}
+
+/// IBEA (Indicator-Based EA) using the additive ε-indicator.
+#[derive(Debug, Clone)]
+pub struct Ibea<I, V> {
+    /// Algorithm configuration.
+    pub config: IbeaConfig,
+    /// Initial-decision sampler.
+    pub initializer: I,
+    /// Offspring-producing variation operator.
+    pub variation: V,
+}
+
+impl<I, V> Ibea<I, V> {
+    /// Construct an `Ibea` optimizer.
+    pub fn new(config: IbeaConfig, initializer: I, variation: V) -> Self {
+        Self { config, initializer, variation }
+    }
+}
+
+impl<P, I, V> Optimizer<P> for Ibea<I, V>
+where
+    P: Problem + Sync,
+    P::Decision: Send,
+    I: Initializer<P::Decision>,
+    V: Variation<P::Decision>,
+{
+    fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision> {
+        assert!(self.config.population_size > 0, "Ibea population_size must be > 0");
+        assert!(self.config.kappa > 0.0, "Ibea kappa must be > 0");
+        let n = self.config.population_size;
+        let objectives = problem.objectives();
+        let mut rng = rng_from_seed(self.config.seed);
+
+        // Initial population.
+        let initial_decisions = self.initializer.initialize(n, &mut rng);
+        let mut population: Vec<Candidate<P::Decision>> =
+            evaluate_batch(problem, initial_decisions);
+        let mut evaluations = population.len();
+
+        for _ in 0..self.config.generations {
+            // --- Phase 1: parent selection (binary tournament on fitness) ---
+            let fitness = compute_fitness(&population, &objectives, self.config.kappa);
+            let mut offspring_decisions: Vec<P::Decision> = Vec::with_capacity(n);
+            while offspring_decisions.len() < n {
+                let p1 = binary_tournament(&fitness, &mut rng);
+                let p2 = binary_tournament(&fitness, &mut rng);
+                let parents = vec![population[p1].decision.clone(), population[p2].decision.clone()];
+                let children = self.variation.vary(&parents, &mut rng);
+                assert!(!children.is_empty(), "Ibea variation returned no children");
+                for child in children {
+                    if offspring_decisions.len() >= n {
+                        break;
+                    }
+                    offspring_decisions.push(child);
+                }
+            }
+
+            // --- Phase 2: parallel-friendly batch evaluation ---
+            let offspring = evaluate_batch(problem, offspring_decisions);
+            evaluations += offspring.len();
+
+            // --- Phase 3: combine + indicator-based survival ---
+            let mut combined: Vec<Candidate<P::Decision>> = Vec::with_capacity(2 * n);
+            combined.extend(population);
+            combined.extend(offspring);
+            population = environmental_selection(combined, &objectives, n, self.config.kappa);
+        }
+
+        let front = pareto_front(&population, &objectives);
+        let best = best_candidate(&population, &objectives);
+        OptimizationResult::new(
+            Population::new(population),
+            front,
+            best,
+            evaluations,
+            self.config.generations,
+        )
+    }
+}
+
+/// Iteratively remove the worst-fitness member from `pool` until `n` remain.
+///
+/// IBEA's standard "subtract the dropped member's contribution from every
+/// survivor's fitness" recomputation is implemented here so we don't have
+/// to rebuild the full O(N²·M) indicator matrix each removal.
+fn environmental_selection<D: Clone>(
+    mut pool: Vec<Candidate<D>>,
+    objectives: &ObjectiveSpace,
+    n: usize,
+    kappa: f64,
+) -> Vec<Candidate<D>> {
+    if pool.len() <= n {
+        return pool;
+    }
+    let oriented: Vec<Vec<f64>> = pool
+        .iter()
+        .map(|c| objectives.as_minimization(&c.evaluation.objectives))
+        .collect();
+
+    // Indicator matrix: indicator[i][j] = max_k (oriented[i][k] - oriented[j][k]).
+    let indicator: Vec<Vec<f64>> = (0..pool.len())
+        .map(|i| {
+            (0..pool.len())
+                .map(|j| {
+                    if i == j {
+                        0.0
+                    } else {
+                        oriented[i]
+                            .iter()
+                            .zip(oriented[j].iter())
+                            .map(|(a, b)| a - b)
+                            .fold(f64::NEG_INFINITY, f64::max)
+                    }
+                })
+                .collect()
+        })
+        .collect();
+    // Normalize indicator by its global magnitude to keep exp() sane.
+    let mut max_abs = 1e-12_f64;
+    for row in &indicator {
+        for &v in row {
+            if v.abs() > max_abs {
+                max_abs = v.abs();
+            }
+        }
+    }
+
+    // Fitness F(i) = -Σ_{j≠i} exp(-indicator[j][i] / (max_abs · kappa)).
+    // (Higher is better — so a candidate dominated by many is heavily negative.)
+    let scale = max_abs * kappa;
+    let mut fitness: Vec<f64> = (0..pool.len())
+        .map(|i| {
+            (0..pool.len())
+                .filter(|&j| j != i)
+                .map(|j| -(-indicator[j][i] / scale).exp())
+                .sum()
+        })
+        .collect();
+
+    let mut alive: Vec<bool> = vec![true; pool.len()];
+    let mut alive_count = pool.len();
+    while alive_count > n {
+        // Find the lowest-fitness alive member.
+        let mut worst = usize::MAX;
+        for i in 0..pool.len() {
+            if !alive[i] {
+                continue;
+            }
+            if worst == usize::MAX || fitness[i] < fitness[worst] {
+                worst = i;
+            }
+        }
+        // Remove its contribution from every other survivor's fitness.
+        for i in 0..pool.len() {
+            if !alive[i] || i == worst {
+                continue;
+            }
+            fitness[i] += (-indicator[worst][i] / scale).exp();
+        }
+        alive[worst] = false;
+        alive_count -= 1;
+    }
+
+    // Materialize survivors, in original order.
+    let mut survivors = Vec::with_capacity(n);
+    for (i, c) in pool.drain(..).enumerate() {
+        if alive[i] {
+            survivors.push(c);
+        }
+    }
+    survivors
+}
+
+/// Compute IBEA fitness without mutating, for use in tournament selection.
+fn compute_fitness<D>(
+    pool: &[Candidate<D>],
+    objectives: &ObjectiveSpace,
+    kappa: f64,
+) -> Vec<f64> {
+    if pool.is_empty() {
+        return Vec::new();
+    }
+    let oriented: Vec<Vec<f64>> = pool
+        .iter()
+        .map(|c| objectives.as_minimization(&c.evaluation.objectives))
+        .collect();
+    let indicator: Vec<Vec<f64>> = (0..pool.len())
+        .map(|i| {
+            (0..pool.len())
+                .map(|j| {
+                    if i == j {
+                        0.0
+                    } else {
+                        oriented[i]
+                            .iter()
+                            .zip(oriented[j].iter())
+                            .map(|(a, b)| a - b)
+                            .fold(f64::NEG_INFINITY, f64::max)
+                    }
+                })
+                .collect()
+        })
+        .collect();
+    let mut max_abs = 1e-12_f64;
+    for row in &indicator {
+        for &v in row {
+            if v.abs() > max_abs {
+                max_abs = v.abs();
+            }
+        }
+    }
+    let scale = max_abs * kappa;
+    (0..pool.len())
+        .map(|i| {
+            (0..pool.len())
+                .filter(|&j| j != i)
+                .map(|j| -(-indicator[j][i] / scale).exp())
+                .sum()
+        })
+        .collect()
+}
+
+fn binary_tournament(fitness: &[f64], rng: &mut Rng) -> usize {
+    let a = rng.random_range(0..fitness.len());
+    let b = rng.random_range(0..fitness.len());
+    if fitness[a] > fitness[b] {
+        a
+    } else if fitness[a] < fitness[b] {
+        b
+    } else if rng.random_bool(0.5) {
+        a
+    } else {
+        b
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::operators::{
+        CompositeVariation, PolynomialMutation, RealBounds, SimulatedBinaryCrossover,
+    };
+    use crate::tests_support::SchafferN1;
+
+    fn make_optimizer(
+        seed: u64,
+    ) -> Ibea<RealBounds, CompositeVariation<SimulatedBinaryCrossover, PolynomialMutation>> {
+        let bounds = vec![(-5.0, 5.0)];
+        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),
+        };
+        Ibea::new(
+            IbeaConfig { population_size: 20, generations: 15, kappa: 0.05, seed },
+            initializer,
+            variation,
+        )
+    }
+
+    #[test]
+    fn produces_pareto_front() {
+        let mut opt = make_optimizer(1);
+        let r = opt.run(&SchafferN1);
+        assert!(!r.pareto_front.is_empty());
+        assert_eq!(r.population.len(), 20);
+    }
+
+    #[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 = "population_size must be > 0")]
+    fn zero_population_size_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 = Ibea::new(
+            IbeaConfig { population_size: 0, generations: 1, kappa: 0.05, seed: 0 },
+            initializer,
+            variation,
+        );
+        let _ = opt.run(&SchafferN1);
+    }
+}

src/algorithms/mod.rs

@@ -4,6 +4,7 @@ pub mod cma_es;
 pub mod differential_evolution;
 pub mod genetic_algorithm;
 pub mod hill_climber;
+pub mod ibea;
 pub mod moead;
 pub mod mopso;
 pub mod nsga2;
@@ -20,6 +21,7 @@ pub use cma_es::*;
 pub use differential_evolution::*;
 pub use genetic_algorithm::*;
 pub use hill_climber::*;
+pub use ibea::*;
 pub use moead::*;
 pub use mopso::*;
 pub use nsga2::*;

src/prelude.rs

@@ -23,8 +23,8 @@ pub use crate::operators::{
 
 pub use crate::algorithms::{
     CmaEs, CmaEsConfig, DifferentialEvolution, DifferentialEvolutionConfig,
-    GeneticAlgorithm, GeneticAlgorithmConfig, HillClimber, HillClimberConfig, Moead,
+    GeneticAlgorithm, GeneticAlgorithmConfig, HillClimber, HillClimberConfig, Ibea,
-    MoeadConfig, Mopso, MopsoConfig, Nsga2, Nsga2Config, Nsga3, Nsga3Config, Paes, PaesConfig, ParticleSwarm,
+    IbeaConfig, Moead, MoeadConfig, Mopso, MopsoConfig, Nsga2, Nsga2Config, Nsga3, Nsga3Config, Paes, PaesConfig, ParticleSwarm,
     ParticleSwarmConfig, RandomSearch, RandomSearchConfig, SimulatedAnnealing,
     SimulatedAnnealingConfig, Spea2, Spea2Config, TabuSearch, TabuSearchConfig,
 };