pso — particle swarm optimization (sampler)

Role: optimization · kind: n4m_sampler_kind_t = N4M_SAMPLER_PSO · since: ABI 2.1 (F3)

Particle Swarm Optimization over the unit hypercube (Kennedy & Eberhart). A swarm of swarm_size (= 16 in F3) particles — each with a position u ∈ [0,1)^P, a velocity, and a remembered personal best — is asked out per iteration. Once the iteration’s trials are scored, each particle’s personal best and the swarm’s global best update, velocities move toward them, and positions advance:

v ← w·v + c1·r1·(pbest − x) + c2·r2·(gbest − x)      x ← clamp01(x + v)

with the Clerc & Kennedy (2002) convergence constants w = 0.729, c1 = c2 = 1.494. Candidates are decoded with the shared Optimizer::decode_candidate, so mixed continuous / discrete / categorical spaces work (sorted-tuple axes fall back to the base sampler; hard constraints are handled via fitness).

Synchronous update (F3): the swarm advances only once its whole iteration is scored (liar = none), so ask_batch returns a partial batch at an iteration boundary. Velocities are capped at vmax = 0.5 of the unit range. Warm-start (n4m_optimizer_enqueue) is not supported for population samplers (returns N4M_ERR_UNSUPPORTED).

PSO is a good default for smooth-ish continuous and mixed surfaces; it complements the more disruptive ga. It reuses the same async-population lifecycle as ga (keyed on trial-id ranges).

HPO-sampler PSO over the typed space — distinct from the feature-selection n4m_feature_selection_pso_select (binary PSO over feature masks).

Usage (C ABI)

n4m_optimizer_options_t opts;
n4m_optimizer_options_init(&opts);
opts.sampler = N4M_SAMPLER_PSO;
opts.seed = 42;

Parity

  • Tier B-state: the swarm trajectory is a deterministic function of the seed + reported fitnesses; identical across bindings at a fixed seed via the shared n4m_rng. Convergence on a continuous test objective is verified in the C++ tests.

References

  • Kennedy & Eberhart, A discrete binary version of the particle swarm algorithm, IEEE SMC (1997); Clerc & Kennedy (2002) convergence constants. See _finetuning_bibliography.bib.