Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Otimização Harris Hawks (HHO)× | Algoritmo do Bolor Limoso× | |
|---|---|---|
| Área | Otimização | Otimização |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2019 | 2020 |
| Autor original≠ | Ali Asghar Heidari | Shimin Li |
| Tipo | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes | HHO | SMA |
| Relacionados≠ | 4 | 5 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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