Comparer des méthodes

	Optimisation bayésienne par essaims particulaires	Optimisation par essaim particulaire (PSO)
Domaine	Simulation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2003	1995
Auteur d'origine	Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO)	—
Type	Hybrid metaheuristic — Bayesian probabilistic swarm search	Population-based metaheuristic / swarm intelligence
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. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	Bayesian PSO, BPSO, Probabilistic Swarm Optimization, Prior-guided PSO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
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.	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.