feat(algorithms): add AgeMoea (Adaptive Geometry Estimation MOEA)

Panichella 2019 AGE-MOEA: a many-objective MOEA that *infers* the
front's geometry (its L_p shape, where p = 1 is linear, p = 2 is
spherical, p < 1 is convex etc.) from the current non-dominated set
and uses that estimate to drive both proximity and diversity in
survival selection.

Each generation:
- NSGA-II-like loop: random parent selection + variation + evaluation
- Combine + non_dominated_sort
- Fill front-by-front; for the splitting front:
  - Translate by ideal point z*
  - Find extreme points by ASF (same as NSGA-III) and intercepts
  - Estimate the geometry parameter p by minimizing
    \|f − ideal\|_p constancy on the extreme points
  - Score every member by survival_score = (proximity_to_ideal) +
    (1 / nearest-neighbor distance in the same L_p frame)
  - Keep the top scorers

The geometry estimation is the novel contribution; with 3+ objectives
it produces fronts whose spread better matches the true shape than
NSGA-III's reference points (which assume a known geometry).

src/prelude.rs

@@ -22,7 +22,7 @@ pub use crate::operators::{
 };
 
 pub use crate::algorithms::{
-    AntColonyTsp, AntColonyTspConfig, CmaEs, CmaEsConfig, DifferentialEvolution,
+    AgeMoea, AgeMoeaConfig, AntColonyTsp, AntColonyTspConfig, CmaEs, CmaEsConfig, DifferentialEvolution,
     DifferentialEvolutionConfig, EpsilonMoea, EpsilonMoeaConfig,
     GeneticAlgorithm, GeneticAlgorithmConfig, HillClimber, HillClimberConfig, Hype,
     HypeConfig, Ibea, IbeaConfig, Moead, MoeadConfig, Mopso, MopsoConfig, Nsga2,