Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Algoritmo de la Luciérnaga× | Grey Wolf Optimizer× | |
|---|---|---|
| Campo | Optimización | Optimización |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2008 | 2014 |
| Autor original≠ | Xin-She Yang | Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis |
| Tipo≠ | Swarm intelligence metaheuristic | Swarm-intelligence metaheuristic |
| Fuente seminal≠ | Yang, 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 ↗ |
| Alias | FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm) | GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO) |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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