Comparer des méthodes

	Optimisation robuste par essaims particulaires	Optimisation par essaim particulaire (PSO)
Domaine	Simulation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s	1995
Auteur d'origine	Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s	—
Type	Metaheuristic — robust swarm-based optimizer	Population-based metaheuristic / swarm intelligence
Source fondatrice	Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers. ISBN: 9781558605954	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Apparentées	6	6
Résumé	Robust Particle Swarm Optimization (Robust PSO) extends the classical PSO metaheuristic to explicitly account for uncertainty in the objective function, constraints, or decision variables. Rather than optimizing a single nominal objective, each candidate solution is evaluated over a set of uncertainty scenarios, and fitness is judged by a robustness criterion such as worst-case performance or expected value, yielding solutions that remain near-optimal even when conditions deviate from nominal assumptions.	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.