Comparar métodos
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| Algoritmo del Tejón de Miel× | Optimización de Gavilanes de Harris× | |
|---|---|---|
| Campo | Optimización | Optimización |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2023 | 2019 |
| Autor original≠ | Fatma A. Hashim | Ali Asghar Heidari |
| Tipo | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| Fuente seminal≠ | Hashim, F. A., Hussain, K., & Houssein, E. H. (2023). Honey badger algorithm: A new meta-heuristic optimization algorithm. Neural Computing and Applications, 35(17), 12265-12287. link ↗ | 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 | HBA | HHO |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | The Honey Badger Algorithm (HBA) is a nature-inspired metaheuristic optimization algorithm presented by Hashim et al. in 2023, modeled on the hunting behavior and intelligent strategies of honey badgers (Mellivora capensis). Honey badgers are known for their remarkable problem-solving abilities, fearlessness, and persistent pursuit of prey and food sources despite significant obstacles. HBA captures these behavioral traits to create an effective optimization framework. | 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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