混乱的对立学习与镜子反射和最糟糕的个人干扰灰狼优化器连续的全球数值优化优化
Oluwatayomi Rereloluwa Adegboye1, Afi Kekeli Feda2, Opeoluwa Seun Ojekemi3
1Management Information Systems, University of Mediterranean Karpasia, Mersin-10, Turkey.
Scientific reports
|February 26, 2024
概括
本研究介绍了CMWGWO,一种增强的灰狼优化器 (GWO) 算法,它克服了局部最佳并改善了种群多样性. CMWGWO表现出卓越的融合精度和对数值挑战的强大优化.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式技术 超启发式技术
背景情况:
- 灰狼优化器 (GWO) 是一个有效的元启发算法.
- 由于对阿尔法狼的依赖,GWO遭受过早的融合和陷入局部最佳状态.
- 在停滞期间缺乏人口多样性限制了GWO的全球搜索能力.
研究的目的:
- 增强灰狼优化器 (GWO) 算法,以改善数值优化.
- 解决GWO的局限性,特别是局部最佳捕获和人口多样性.
- 引入一个新的算法,CMWGWO,结合混沌对立学习 (COL),镜像反射策略 (MRS) 和最坏的个人干扰 (WID).
主要方法:
- 整合混乱的反对学习 (COL) 加强多样化和减少停滞.
- 实施镜像反射策略 (MRS) 以扩大勘探范围并加强全球搜索.
- 纳入最坏的个人干扰 (WID) 以改善信息交换和逃离局部最佳状态.
主要成果:
- 与原来的GWO相比,CMWGWO表现出更高的性能.
- 改进的算法显示了更好的收精度和强大的优化能力.
- 对23个基准函数,10个CEC19函数和3个工程问题进行了实验.
结论:
- CMWGWO有效地克服了标准GWO算法的局限性.
- 拟议的改进显著提高了勘探潜力和搜索精度.
- 对于数值优化挑战,CMWGWO提供了更有效和更强大的解决方案.
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