Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Grey Wolf Optimizer× | Algoritmo Genético× | |
|---|---|---|
| Campo | Optimización | Optimización |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2014 | 1975 |
| Autor original≠ | Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis | John Henry Holland |
| Tipo≠ | Swarm-intelligence metaheuristic | Population-based metaheuristic |
| Fuente seminal≠ | Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI ↗ | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| Alias | GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO) | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. | A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail. |
| ScholarGateConjunto de datos ↗ |
|
|