Comparer des méthodes

	Optimisation robuste par essaims particulaires	Algorithme Génétique Robuste
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s	2005 (systematic survey); earlier applications from late 1990s
Auteur d'origine	Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s	Jin, Y. and Branke, J. (systematic formalization); roots in Holland (1975)
Type	Metaheuristic — robust swarm-based optimizer	Metaheuristic evolutionary optimizer with robustness mechanism
Source fondatrice	Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers. ISBN: 9781558605954	Jin, Y., Branke, J. (2005). Evolutionary optimization in uncertain environments — a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317.
Alias	Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness	RGA, Robust GA, Uncertainty-Aware Genetic Algorithm, Noise-Tolerant Genetic Algorithm
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.	The Robust Genetic Algorithm (RGA) extends standard genetic algorithms to find solutions that perform well not only at the nominal design point but also when subjected to uncertainty in decision variables, parameters, or fitness evaluations. By incorporating explicit robustness measures into selection pressure, RGA balances optimality against sensitivity to perturbation, making it suitable for engineering design, scheduling, and policy optimization under real-world variability.