Comparer des méthodes

	Optimisation par Essaim de Loups Gris	Optimisation par essaim particulaire (PSO)
Domaine	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2014	1995
Auteur d'origine	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis	—
Type	Swarm-intelligence metaheuristic	Population-based metaheuristic / swarm intelligence
Source fondatrice	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Apparentées	5	6
Résumé	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.	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.