Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Optymalizacja metodą sokołów Harris× | Algorytm Optymalizacji Wilków Szarych× | |
|---|---|---|
| Dziedzina | Optymalizacja | Optymalizacja |
| Rodzina≠ | Machine learning | Process / pipeline |
| Rok powstania≠ | 2019 | 2014 |
| Twórca≠ | Ali Asghar Heidari | Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis |
| Typ≠ | Nature-inspired metaheuristic algorithm | Swarm-intelligence metaheuristic |
| Źródło pierwotne≠ | 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 ↗ | Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI ↗ |
| Inne nazwy≠ | HHO | GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO) |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | 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. | 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. |
| ScholarGateZbiór danych ↗ |
|
|