方法对比
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| 鲸鱼优化算法 (WOA)× | 灰狼优化算法× | |
|---|---|---|
| 领域 | 优化 | 优化 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2016 | 2014 |
| 提出者≠ | Seyedali Mirjalili & Andrew Lewis | Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis |
| 类型≠ | Swarm-based metaheuristic | Swarm-intelligence metaheuristic |
| 开创性文献≠ | Mirjalili, S. & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67. DOI ↗ | Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI ↗ |
| 别名 | WOA, Balina Optimizasyon Algoritması (WOA), bubble-net attacking method | GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO) |
| 相关 | 5 | 5 |
| 摘要≠ | The Whale Optimization Algorithm (WOA) is a swarm-based metaheuristic introduced by Mirjalili and Lewis in 2016. It models the bubble-net hunting strategy of humpback whales, in which a group of whales spirals around prey while gradually tightening the encirclement. The algorithm balances global exploration and local exploitation through a small set of parameters and has become widely used in continuous engineering optimisation problems. | 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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