build(bench): expand gungraun bench suite to cover every algorithm
Goes from 6 benchmarks to ~25:
Pareto utilities (existing):
- non_dominated_sort_2d (n=50, 200)
- crowding_distance_2d (n=50, 200)
- hypervolume_2d (n=30, 100)
- hypervolume_nd_3d (n=30, 100)
Single-objective algorithms (all measured at "one short run"):
- random_search, hill_climber, one_plus_one_es, simulated_annealing
- genetic_algorithm, particle_swarm, differential_evolution, tlbo
- cma_es, separable_nes, nelder_mead, bayesian_opt, tpe
Multi-objective algorithms (one short run each):
- nsga2, nsga3, spea2, moead, mopso, ibea, sms_emoa
- hype, pesa2, epsilon_moea, age_moea, grea, knea, rvea
Each uses a tiny problem with realistic-shape parameters (small pop,
few generations, tight bounds) so the benchmark exercises each
algorithm's *inner loop cost* rather than dominated by RNG init or
config parsing.
swaits
aba9dbf469