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| ドワーフマングース最適化× | 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≠ | 2022 | 2019 |
| 提唱者≠ | Joseph O. Agushaka | Ali Asghar Heidari |
| 種類 | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| 原典≠ | Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570. 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 | DMO | HHO |
| 関連 | 4 | 4 |
| 概要≠ | The Dwarf Mongoose Optimization (DMO) algorithm is a nature-inspired metaheuristic introduced by Agushaka et al. in 2022, based on the behavioral patterns of dwarf mongoose colonies. Dwarf mongooses exhibit sophisticated group dynamics including sentry behavior (surveillance and exploration), pup care (mentoring), and cooperative hunting. The algorithm translates these social behaviors into optimization mechanisms that balance exploration and exploitation effectively. | 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. |
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