feat(algorithms): add KnEA (Knee point-driven EA)

Zhang, Tian & Jin 2015 KnEA: many-objective MOEA that biases survival
selection toward 'knee points' on the Pareto front — points where a
small improvement in one objective costs a large degradation in
another.

Each generation:
- NSGA-II-like loop with offspring + non_dominated_sort
- For the splitting front, identify knee points by perpendicular
  distance from the hyperplane connecting the front's extreme points.
  Members further from the hyperplane (= more 'kneeness') are preferred.
- Survival keeps every knee-tagged member; if room remains, fill from
  remaining members by largest perpendicular distance.

Knee points are intuitively the most attractive points on a Pareto
front when no preference information is available. KnEA pushes the
search toward them at the cost of less uniform front coverage.

src/algorithms/tlbo.rs

@@ -142,7 +142,7 @@ where
 
         let final_pop: Vec<Candidate<Vec<f64>>> = decisions
             .into_iter()
-            .zip(evals.into_iter())
+            .zip(evals)
             .map(|(d, e)| Candidate::new(d, e))
             .collect();
         let best = best_candidate(&final_pop, &objectives);