Comparer des méthodes

	Algorithme des lucioles	Évolution Différentielle	Optimisation par Essaim de Loups Gris
Domaine	Optimisation	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline	Process / pipeline
Année d'origine	2008	1997	2014
Auteur d'origine	Xin-She Yang	Rainer Storn & Kenneth Price	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Type	Swarm intelligence metaheuristic	Population-based stochastic 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.	Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359.	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)	DE algorithm, Diferansiyel Evrim (DE), DE optimization	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Apparentées	5	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.	Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods.	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.