feat(algorithms): add NSGA-III

Implementation of Deb & Jain 2014 NSGA-III — the canonical
many-objective MOEA. Replaces NSGA-II's crowding-distance niching
with a structured reference-point niching procedure that scales to
3+ objectives where crowding distance loses its diversity signal.

Each generation:
1. Random parent selection + variation + offspring evaluation, same as
   NSGA-II.
2. Combine + non_dominated_sort, fill the next population front-by-
   front until the next front would overflow (the splitting front F_l).
3. Survival on F_l uses reference-point niching:
   - Translate by the ideal point z* (per-axis min in oriented space).
   - Compute extreme points by ASF and intercepts; normalize by
     intercepts (with a robust fallback to per-axis range if extreme
     points are degenerate).
   - Associate every member of the working pool with the closest
     reference direction by perpendicular distance.
   - Iteratively pick from F_l: prefer the niche with the smallest
     count among references that have F_l candidates; if the niche is
     empty in the already-selected set, take the closest associated
     member by perpendicular distance, otherwise pick uniformly from
     the niche.

Public API:

  Nsga3Config { population_size, generations, reference_divisions, seed }
  Nsga3 { config, initializer, variation }
  impl<P, I, V> Optimizer<P> for Nsga3<I, V>

Re-exported from the prelude. Tests cover non-empty Pareto front,
exact final population size, deterministic reruns, and panic on
`population_size == 0`. Uses the existing tests_support problems.

src/algorithms/mod.rs

@@ -2,6 +2,7 @@
 
 pub mod differential_evolution;
 pub mod nsga2;
+pub mod nsga3;
 pub mod paes;
 pub(crate) mod parallel_eval;
 pub mod random_search;
@@ -9,6 +10,7 @@ pub mod spea2;
 
 pub use differential_evolution::*;
 pub use nsga2::*;
+pub use nsga3::*;
 pub use paes::*;
 pub use random_search::*;
 pub use spea2::*;