Process / pipeline
灰狼优化算法 — GWO
灰狼优化算法（GWO）是一种群体智能元启发式算法，由Mirjalili、Mirjalili和Lewis于2014年提出，它模拟了灰狼的社会等级和合作狩猎行为。候选解的种群被划分为四个领导等级——头狼（alpha）、二头狼（beta）、三头狼（delta）和普通狼（omega），每一迭代中最好的三个解会引导整个狼群趋向搜索空间中越来越优的区域。
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来源
- Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI: 10.1016/j.advengsoft.2013.12.007 ↗
- Faris, H., Aljarah, I., Al-Betar, M. A., & Mirjalili, S. (2018). Grey Wolf Optimizer: A Review of Recent Variants and Applications. Neural Computing and Applications, 30(2), 413-435. DOI: 10.1007/s00521-017-3272-5 ↗
如何引用本页
ScholarGate. (2026, June 1). Grey Wolf Optimizer (GWO). ScholarGate. https://scholargate.app/zh/optimization/grey-wolf-optimizer
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