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Firefly Algorithm×Grey Wolf Optimizer×
CampOptimitzacióOptimització
FamíliaProcess / pipelineProcess / pipeline
Any d'origen20082014
Autor originalXin-She YangSeyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
TipusSwarm intelligence metaheuristicSwarm-intelligence metaheuristic
Font seminalYang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84. DOI ↗Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI ↗
ÀliesFA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Relacionats55
ResumThe 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.
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ScholarGateCompara mètodes: Firefly Algorithm · Grey Wolf Optimizer. Recuperat el 2026-06-15 de https://scholargate.app/ca/compare