方法对比
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| 矮獴优化算法× | 灰狼优化算法× | |
|---|---|---|
| 领域 | 优化 | 优化 |
| 方法族≠ | Machine learning | Process / pipeline |
| 起源年份≠ | 2022 | 2014 |
| 提出者≠ | Joseph O. Agushaka | Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis |
| 类型≠ | Nature-inspired metaheuristic algorithm | Swarm-intelligence metaheuristic |
| 开创性文献≠ | Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570. DOI ↗ | Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI ↗ |
| 别名≠ | DMO | GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO) |
| 相关≠ | 4 | 5 |
| 摘要≠ | 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. | The Grey Wolf Optimizer (GWO) is a swarm-intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014 that models the social hierarchy and cooperative hunting behaviour of grey wolves. A population of candidate solutions is divided into four leadership ranks — alpha, beta, delta, and omega — and the three best solutions at each iteration guide the entire swarm toward increasingly better regions of the search space. |
| ScholarGate数据集 ↗ |
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