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/prelude.rs

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