Comparer des méthodes

	Algorithme Génétique Robuste	Optimisation robuste par essaims particulaires
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2005 (systematic survey); earlier applications from late 1990s	2000s
Auteur d'origine	Jin, Y. and Branke, J. (systematic formalization); roots in Holland (1975)	Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s
Type	Metaheuristic evolutionary optimizer with robustness mechanism	Metaheuristic — robust swarm-based optimizer
Source fondatrice	Jin, Y., Branke, J. (2005). Evolutionary optimization in uncertain environments — a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317.	Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers. ISBN: 9781558605954
Alias	RGA, Robust GA, Uncertainty-Aware Genetic Algorithm, Noise-Tolerant Genetic Algorithm	Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness
Apparentées	6	6
Résumé	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.	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.