Comparer des méthodes

	Algorithme Génétique Bayésien	Optimisation par essaim particulaire (PSO)
Domaine	Simulation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1999	1995
Auteur d'origine	Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.	—
Type	Evolutionary metaheuristic with Bayesian probabilistic model	Population-based metaheuristic / swarm intelligence
Source fondatrice	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.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	BGA, Bayesian-guided GA, Probabilistic GA, EDA-GA	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Apparentées	5	6
Résumé	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.	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.