Comparer des méthodes

	Optimisation bayésienne par essaims particulaires	Optimisation robuste par essaims particulaires
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2003	2000s
Auteur d'origine	Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO)	Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s
Type	Hybrid metaheuristic — Bayesian probabilistic swarm search	Metaheuristic — robust swarm-based optimizer
Source fondatrice	Higashi, N., Iba, H. (2003). Particle swarm optimization with Gaussian mutation. Proceedings of the 2003 IEEE Swarm Intelligence Symposium, Indianapolis, IN, USA, pp. 72-79.	Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers. ISBN: 9781558605954
Alias	Bayesian PSO, BPSO, Probabilistic Swarm Optimization, Prior-guided PSO	Robust PSO, RPSO, Uncertainty-robust PSO, PSO with robustness
Apparentées	6	6
Résumé	Bayesian Particle Swarm Optimization (Bayesian PSO) integrates Bayesian probabilistic reasoning into the standard particle swarm framework. Particles update their velocities and positions guided not only by personal and global best positions but also by a Bayesian posterior that encodes prior knowledge about the solution space, enabling more directed and statistically principled exploration of complex optimization landscapes.	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.