feat(algorithms): add HypE (Hypervolume Estimation)

Bader & Zitzler 2011: HypE estimates hypervolume contributions via
Monte Carlo sampling instead of computing them exactly. The point of
the trick is that exact hypervolume becomes prohibitively expensive
beyond ~5 objectives, while MC sampling stays cheap and accurate
enough at any dimension.

Each generation:
- Generate offspring via parent selection + variation + evaluation
- Combine, run non_dominated_sort, fill front-by-front
- For the splitting front, estimate each member's HV contribution
  by drawing `n_samples` uniform points in the box [ideal, reference]
  and counting how many points are dominated by *exactly* one front
  member — that count, divided by n_samples and multiplied by the
  box volume, is the member's expected unique HV contribution.
- Drop members one at a time from the splitting front by smallest
  estimated contribution.

Public API matches the rest of the MO algorithms (Config + Optimizer).
The reference point is supplied in the config so the user controls
the integration domain. Tests cover non-empty front, deterministic
reruns, and panic on dim-mismatched reference.

src/algorithms/mod.rs

@@ -5,6 +5,7 @@ pub mod cma_es;
 pub mod differential_evolution;
 pub mod genetic_algorithm;
 pub mod hill_climber;
+pub mod hype;
 pub mod ibea;
 pub mod moead;
 pub mod mopso;
@@ -25,6 +26,7 @@ pub use cma_es::*;
 pub use differential_evolution::*;
 pub use genetic_algorithm::*;
 pub use hill_climber::*;
+pub use hype::*;
 pub use ibea::*;
 pub use moead::*;
 pub use mopso::*;