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一个改进的子搜索算法与多策略集成
Zongyao Wang1, Qiyang Peng1, Wei Rao2,3
1School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China.
Scientific reports
|January 26, 2025
概括
一个新的多策略集成搜索算法 (MISSA) 通过提高搜索能力和避免局部优化来增强优化. 在基准测试中,MISSA在标准Sparrow搜索算法 (SSA) 上表现优越.
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:
- 小搜索算法 (SSA) 患有低收精度和倾向于陷入局部最佳的陷.
- 现有的优化算法需要在搜索能力,局部最佳避免和人口多样性方面进行改进.
研究的目的:
- 提出一种新的多策略集成搜索算法 (MISSA),以克服标准SSA的局限性.
- 增强全球搜索能力,提高逃离局部优化能力,增加优化中的人口多样性.
主要方法:
- 开发了一种改进的搜索策略 (ISS),通过增强黑翼风算法来更新生产者的位置.
- 引入了一种以Coot算法为灵感的集体跟踪策略 (GFS),以改善本地最佳逃生.
- 实施了基于随机对立的学习策略 (ROBLS) 代后,以促进人口多样性.
主要成果:
- 在 24 个基准函数和 CEC 2017 函数上进行了广泛的测试.
- 实验结果和威尔科克森等级总和测试证实了MISSA的卓越性能.
- MISSA的性能优于标准的SSA和其他先进的优化算法.
结论:
- 拟议的MISSA有效地解决了SSA的缺陷,展示了增强的优化能力.
- MISSA表现出优越的整体性能,使其成为复杂优化问题的有希望的算法.
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