feat(algorithms): add Rvea (Reference Vector-guided EA)

Cheng, Jin, Olhofer & Sendhoff 2016 RVEA: many-objective MOEA built
around a fixed set of Das–Dennis reference vectors. Each generation:
- Generate offspring via random parent selection + variation +
  evaluation
- Combine population + offspring; translate by ideal point z*
- Associate every member with the reference vector whose angle to
  the translated objective vector is smallest
- For each occupied vector, keep the member with the smallest
  Angle-Penalized Distance (APD) score; the rest are dropped
- APD = (1 + α(t)·θ_max·γ) · |f − z*| where γ is the angle to the
  associated reference and α(t) = (t / t_max)^2 anneals the angle
  penalty over the run

This produces well-spread fronts at high objective counts where
Pareto-rank methods (NSGA-II, SPEA2) lose discrimination.

src/prelude.rs

@@ -26,7 +26,8 @@ pub use crate::algorithms::{
     DifferentialEvolutionConfig,
     GeneticAlgorithm, GeneticAlgorithmConfig, HillClimber, HillClimberConfig, Hype,
     HypeConfig, Ibea, IbeaConfig, Moead, MoeadConfig, Mopso, MopsoConfig, Nsga2, Nsga2Config, Nsga3, Nsga3Config, Paes, PaesConfig, ParticleSwarm,
-    ParticleSwarmConfig, RandomSearch, RandomSearchConfig, SimulatedAnnealing,
+    ParticleSwarmConfig, RandomSearch, RandomSearchConfig, Rvea, RveaConfig,
+    SimulatedAnnealing,
     SimulatedAnnealingConfig, SmsEmoa, SmsEmoaConfig, Spea2, Spea2Config, TabuSearch,
     TabuSearchConfig, Umda,
     UmdaConfig,