Comparer des méthodes

	Optimisation bayésienne par essaims particulaires	Algorithme Génétique Bayésien
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2003	1999
Auteur d'origine	Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO)	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.
Type	Hybrid metaheuristic — Bayesian probabilistic swarm search	Evolutionary metaheuristic with Bayesian probabilistic model
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.	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), pp. 525–532. Morgan Kaufmann.
Alias	Bayesian PSO, BPSO, Probabilistic Swarm Optimization, Prior-guided PSO	BGA, Bayesian-guided GA, Probabilistic GA, EDA-GA
Apparentées	6	5
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.	A Bayesian Genetic Algorithm (BGA) replaces traditional crossover and mutation operators with a probabilistic Bayesian network learned from selected high-fitness individuals. At each generation the algorithm builds a graphical model of promising solution structure, then samples new offspring from that model, enabling the search to capture and exploit variable dependencies that standard GAs miss.