Comparer des méthodes

	Optimisation par essaim particulaire déterministe	Optimisation par essaim particulaire stochastique
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1995 (PSO); deterministic formulation circa 2002	1995–2002
Auteur d'origine	Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature	Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community
Type	Swarm intelligence metaheuristic — deterministic variant	Metaheuristic optimization — stochastic swarm intelligence
Source fondatrice	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 — International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE.	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE.
Alias	DPSO, Deterministic PSO, PSO without stochastic components, Fully Deterministic PSO	Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO
Apparentées	6	4
Résumé	Deterministic Particle Swarm Optimization (DPSO) removes the stochastic random coefficients from classical PSO, replacing them with fixed cognitive and social acceleration parameters. Particles move through the search space following fully predictable trajectories, enabling reproducible convergence analysis and guaranteed termination behavior in continuous and combinatorial optimization problems.	Stochastic Particle Swarm Optimization (Stochastic PSO) is a swarm-intelligence metaheuristic that extends the standard PSO framework by incorporating explicit stochastic elements — random inertia weights, probabilistic velocity resets, or noise injections — to escape local optima and maintain population diversity throughout the search. It is widely applied to continuous, mixed, and noisy optimization problems in engineering, operations research, and simulation-based design.