Comparer des méthodes

	Optimisation par essaim particulaire stochastique	Optimisation stochastique multi-objectifs
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1995–2002	1990s–2000s
Auteur d'origine	Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community	Various (Fonseca, Fleming, Deb, Zitzler, and others)
Type	Metaheuristic optimization — stochastic swarm intelligence	Stochastic metaheuristic optimization
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.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Alias	Stochastic PSO, SPSO, Randomized PSO, Probabilistic PSO	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization
Apparentées	4	5
Résumé	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.	Stochastic Multi-Objective Optimization (SMOO) is a class of methods that simultaneously optimizes two or more conflicting objectives when parameters, costs, or constraints are uncertain or random. Rather than a single optimal solution, it produces a Pareto front of non-dominated solutions, each representing a different balance among objectives under the modeled uncertainty.