手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 粘菌アルゴリズム× | ハリスホーク最適化× | |
|---|---|---|
| 分野 | 最適化 | 最適化 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2020 | 2019 |
| 提唱者≠ | Shimin Li | Ali Asghar Heidari |
| 種類 | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| 原典≠ | 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 ↗ | 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 ↗ |
| 別名 | SMA | HHO |
| 関連≠ | 5 | 4 |
| 概要≠ | 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. | 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. |
| ScholarGateデータセット ↗ |
|
|