feat(algorithms): add AntColonyTsp ant colony optimization for TSP-style permutations

Dorigo-style Ant System for permutation problems on a complete graph:
each generation, every ant constructs a tour by probabilistically
picking the next node from those it has not yet visited, weighted by
`τ_ij^α · η_ij^β` where τ is the pheromone level on edge (i, j) and
η is the heuristic desirability (1 / distance, here). After all ants
finish, pheromone evaporates by a factor `(1 - ρ)` and is reinforced
on each ant's tour proportional to that tour's quality.

Decision type is `Vec<usize>` (a permutation of 0..n_cities). The user
supplies a distance matrix and the n_cities is inferred. Single-objective
only (the cost is total tour length, which the Problem evaluates).

Tests build a 5-city ring and verify ACO finds a near-optimal tour,
plus deterministic reruns and panic on multi-objective.

src/prelude.rs

@@ -22,7 +22,8 @@ pub use crate::operators::{
 };
 
 pub use crate::algorithms::{
-    CmaEs, CmaEsConfig, DifferentialEvolution, DifferentialEvolutionConfig,
+    AntColonyTsp, AntColonyTspConfig, CmaEs, CmaEsConfig, DifferentialEvolution,
+    DifferentialEvolutionConfig,
     GeneticAlgorithm, GeneticAlgorithmConfig, HillClimber, HillClimberConfig, Ibea,
     IbeaConfig, Moead, MoeadConfig, Mopso, MopsoConfig, Nsga2, Nsga2Config, Nsga3, Nsga3Config, Paes, PaesConfig, ParticleSwarm,
     ParticleSwarmConfig, RandomSearch, RandomSearchConfig, SimulatedAnnealing,