Comparer des méthodes

	Optimisation par essaim particulaire (PSO)	Optimisation par Essaim de Loups Gris
Domaine	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1995	2014
Auteur d'origine	—	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Type	Population-based metaheuristic / swarm intelligence	Swarm-intelligence metaheuristic
Source fondatrice	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
Alias	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Apparentées	6	5
Résumé	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.	The Grey Wolf Optimizer (GWO) is a swarm-intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014 that models the social hierarchy and cooperative hunting behaviour of grey wolves. A population of candidate solutions is divided into four leadership ranks — alpha, beta, delta, and omega — and the three best solutions at each iteration guide the entire swarm toward increasingly better regions of the search space.