Comparer des méthodes

	Optimisation par Essaim de Loups Gris	Recherche Tabou
Domaine	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2014	1989
Auteur d'origine	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis	Fred Glover
Type	Swarm-intelligence metaheuristic	Local-search metaheuristic
Source fondatrice	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.	Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.
Alias	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)	Tabu Araması (Tabu Search), TS, tabu metaheuristic
Apparentées	5	4
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.	Tabu Search is a local-search metaheuristic introduced by Fred Glover in 1989 that uses a tabu list — a short-term memory of recently visited solutions — to prevent cycling and escape local optima. By explicitly forbidding moves that reverse recent decisions, the algorithm explores the search space more broadly and, through long-term memory structures such as aspiration criteria, aims to approach the global optimum even in large, complex combinatorial problems.