Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Optimización por Enjambre de Partículas (PSO)× | Grey Wolf Optimizer× | |
|---|---|---|
| Campo | Optimización | Optimización |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1995 | 2014 |
| Autor original≠ | — | Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis |
| Tipo≠ | Population-based metaheuristic / swarm intelligence | Swarm-intelligence metaheuristic |
| Fuente seminal≠ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ | Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI ↗ |
| Alias | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) | GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO) |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems. | 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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