多策略蜂蜜子算法用于全球优化
Delong Guo1,2, Huajuan Huang3
1School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun 558000, China.
Biomimetics (Basel, Switzerland)
|September 26, 2025
概括
多策略蜂蜜子算法 (MSHBA) 通过改善人口多样性和全球搜索来提高优化. 这种改进的算法在基准函数和工程问题上表现出色.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超听证学是一种超听证学.
背景情况:
- 蜜算法 (HBA) 是一种新的元启发,灵感来自于蜜的食.
- HBA面临的挑战包括缓慢的融合和局部最佳陷.
- 现有的HBA策略与勘探开发平衡作斗争.
研究的目的:
- 为了增强蜂蜜子算法 (HBA) 以提高优化性能.
- 介绍多策略蜂蜜子算法 (MSHBA),以解决HBA的局限性.
- 提高融合速度,全球搜索能力和稳定性.
主要方法:
- 实施立方混沌映射,以增强最初的人口多样性.
- 综合随机搜索,精英接触式搜索和差异突变策略.
- 应用MSHBA到IEEE CEC 2017的基准函数和工程设计问题.
主要成果:
- MSHBA表现出卓越的表现,在29个IEEE CEC 2017基准中的26个中脱而出.
- 统计分析证实了MSHBA的增强性能.
- MSHBA成功解决了四个受限制的工程设计问题.
结论:
- 多策略蜂蜜子算法 (MSHBA) 有效地克服了原始HBA的局限性.
- 对于复杂的优化任务,MSHBA提供了一种强大而高效的方法.
- 拟议的改进显著提高了全球优化能力和趋同.
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