Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Optymalizacja metodą sokołów Harris× | Algorytm śluzowca× | |
|---|---|---|
| Dziedzina | Optymalizacja | Optymalizacja |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2019 | 2020 |
| Twórca≠ | Ali Asghar Heidari | Shimin Li |
| Typ | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| 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 ↗ | Li, S., Chen, H., Wang, M., Heidari, A. A., & Chakraborty, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300-323. DOI ↗ |
| Inne nazwy | HHO | SMA |
| 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 Slime Mould Algorithm (SMA) is a nature-inspired metaheuristic optimization technique introduced by Li et al. in 2020. It mimics the behavior of slime moulds, which spread and contract to find optimal food sources. SMA addresses complex optimization problems by simulating the adaptive foraging and spatial distribution patterns of these organisms. |
| ScholarGateZbiór danych ↗ |
|
|