Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Optimizador Aguila× | Optimización de Gavilanes de Harris× | |
|---|---|---|
| Campo | Optimización | Optimización |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2021 | 2019 |
| Autor original≠ | Laith Abualigah | Ali Asghar Heidari |
| Tipo | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| 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 ↗ | Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872. DOI ↗ |
| Alias | AO | HHO |
| Relacionados≠ | 3 | 4 |
| 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. | Harris Hawks Optimization (HHO) is a metaheuristic algorithm introduced by Heidari et al. in 2019, inspired by the hunting strategies of Harris's hawks. The algorithm models the cooperative hunting behavior and escape strategies of these raptors to solve complex optimization problems. HHO balances exploration through perching and exploitation through dynamic pursuit, making it effective for multimodal and high-dimensional optimization. |
| ScholarGateConjunto de datos ↗ |
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