Comparar métodos
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| Optimizador Aguila× | Optimización por Enjambre de Partículas (PSO)× | |
|---|---|---|
| Campo | Optimización | Optimización |
| Familia≠ | Machine learning | Process / pipeline |
| Año de origen≠ | 2021 | 1995 |
| Autor original≠ | Laith Abualigah | — |
| Tipo≠ | Nature-inspired metaheuristic algorithm | Population-based metaheuristic / swarm intelligence |
| Fuente seminal≠ | Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers and Industrial Engineering, 157, 107250. DOI ↗ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| Alias≠ | AO | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Relacionados≠ | 3 | 6 |
| Resumen≠ | The Aquila Optimizer (AO) is a nature-inspired metaheuristic algorithm presented by Abualigah et al. in 2021, modeled after the hunting behavior and sensory abilities of golden eagles (aquila chrysaetos). The algorithm captures the exploration and exploitation phases of eagle hunting, including high-altitude soaring, exploration with high-precision vision, and rapid diving attacks. AO is designed to solve both constrained and unconstrained optimization problems. | 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. |
| ScholarGateConjunto de datos ↗ |
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