一种准对立学习和混乱的本地搜索,基于对全球优化问题的海优化
Yier Li1, Lei Li2,3, Zhengpu Lian1
1College of Engineering, Zhejiang Normal University, Jinhua, 321000, China.
这项研究介绍了QOCWO,一种增强的海优化 (WO) 算法. QOCWO战胜了WO的胜利.
科学领域:
- 计算智能是一种计算智能.
- 超启发式优化优化
- 算法开发 算法开发
背景情况:
- 摩优化 (WO) 算法虽然有前途,但却存在缓慢的收和局部最佳陷问题.
- 现有的元启发算法需要提高全球搜索能力和融合速度.
研究的目的:
- 通过整合基于准对立的学习和混乱的本地搜索来增强Walrus优化 (WO) 算法.
- 提高全球搜索能力,扩大搜索范围,加快融合.
- 在WO算法中解决过早的收和局部最佳问题.
主要方法:
- 将基于准对立的学习集成到WO算法中.
- 整合一个混乱的本地搜索机制来加速融合.
- 在23个标准基准函数上应用和比较拟定的QOCWO算法.
主要成果:
- 与标准函数上的其他七种算法相比,QOCWO算法表现出更高的性能.
- 使用Wilcoxon等级和值测试进行的统计验证证实了QOCWO的改进的重要性.
- 在两个真实的工程设计问题中,QOCWO实现了成本降低.
结论:
- 拟议的QOCWO算法有效地克服了原来的WO算法的局限性.
- 在理论和实践领域,QOCWO显示出解决复杂优化问题的巨大潜力.
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