feat(algorithms): add MOEA/D with Tchebycheff decomposition

Implementation of Zhang & Li 2007 MOEA/D — the canonical
decomposition-based MOEA. Different paradigm from Pareto-dominance
algorithms: each subproblem is a scalarized single-objective problem
defined by a Das–Dennis weight vector, and subproblems with similar
weight vectors form neighborhoods that share genetic material.

Each generation iterates over every weight vector `i`:
1. Pick two parents uniformly from the T-nearest neighbors of weight i
   (T = neighborhood_size).
2. Apply variation, evaluate the child.
3. Update the ideal point z* with the child's objectives.
4. Walk the entire neighborhood: for each j, if the child's
   Tchebycheff value g(child | w_j, z*) <= g(current[j] | w_j, z*),
   replace current[j] with the child.

Tchebycheff scalarization:

  g(f | w, z*) = max_k w_k · |f_k - z*_k|

(With the standard `w_k = 1e-6` floor when a weight is zero, so the
max well-defined.)

Public API:

  MoeadConfig {
      generations,
      reference_divisions,  // Das-Dennis H, also fixes population size
      neighborhood_size,    // T
      seed,
  }
  Moead { config, initializer, variation }
  impl<P, I, V> Optimizer<P> for Moead<I, V>

Population size equals the number of weight vectors generated by
das_dennis(num_objectives, reference_divisions). Re-exported from the
prelude. Tests cover non-empty Pareto front, deterministic reruns,
and panic on `reference_divisions` that would yield zero weights.

src/prelude.rs

@@ -22,6 +22,7 @@ pub use crate::operators::{
 };
 
 pub use crate::algorithms::{
-    DifferentialEvolution, DifferentialEvolutionConfig, Nsga2, Nsga2Config, Nsga3,
-    Nsga3Config, Paes, PaesConfig, RandomSearch, RandomSearchConfig, Spea2, Spea2Config,
+    DifferentialEvolution, DifferentialEvolutionConfig, Moead, MoeadConfig, Nsga2,
+    Nsga2Config, Nsga3, Nsga3Config, Paes, PaesConfig, RandomSearch, RandomSearchConfig,
+    Spea2, Spea2Config,
 };