Comparer des méthodes

	Algorithme des lucioles	Optimisation par Essaim de Loups Gris
Domaine	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2008	2014
Auteur d'origine	Xin-She Yang	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Type	Swarm intelligence metaheuristic	Swarm-intelligence metaheuristic
Source fondatrice	Yang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84.	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
Alias	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Apparentées	5	5
Résumé	The Firefly Algorithm (FA), introduced by Xin-She Yang in 2008 and formally published in 2010, is a nature-inspired swarm metaheuristic that models the bioluminescent attraction behaviour of fireflies. Each candidate solution is a firefly whose brightness represents its objective-function value; dimmer fireflies move toward brighter ones with an attraction force that decays with distance, driving the swarm toward optima without gradient information.	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.