Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Jellyfish Search Optimizer× | Aquila Optimizer× | |
|---|---|---|
| Área | Otimização | Otimização |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2022 | 2021 |
| Autor original≠ | Xueying Shi | Laith Abualigah |
| Tipo | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| Fonte seminal≠ | Shi, X., Sun, Y., Zhan, Z. H., Yuen, K. F., & Zhang, J. (2022). Jellyfish search optimizer: A new bio-inspired metaheuristic algorithm for solving optimization tasks. Neural Computing and Applications, 34(10), 7651-7673. link ↗ | 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 ↗ |
| Outros nomes | JSO | AO |
| Relacionados | 3 | 3 |
| Resumo≠ | The Jellyfish Search Optimizer (JSO) is a biologically-inspired metaheuristic algorithm introduced by Shi et al. in 2022, based on the movement and foraging behavior of jellyfish in ocean environments. Jellyfish exhibit two distinct behaviors: passive drifting with ocean currents (exploration) and active swimming toward food sources (exploitation). JSO captures these behaviors to create an effective balance between global search and local refinement. | 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. |
| ScholarGateConjunto de dados ↗ |
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