Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Optimisation par Essaim de Loups Gris× | Recuit simulé× | |
|---|---|---|
| Domaine | Optimisation | Optimisation |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 2014 | 1983 |
| Auteur d'origine≠ | Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis | — |
| Type≠ | Swarm-intelligence metaheuristic | Probabilistic metaheuristic / local search |
| Source fondatrice≠ | Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI ↗ | Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI ↗ |
| Alias | GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO) | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search |
| Apparentées | 5 | 5 |
| 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. | Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems. |
| ScholarGateJeu de données ↗ |
|
|