多策略改进的红蓝优化算法及其应用
Yancang Li1,2, Jiaqi Zhi1, Xinle Wang1
1School of Civil Engineering, Hebei University of Engineering, Handan 056038, China.
Biomimetics (Basel, Switzerland)
|September 26, 2025
概括
这项研究引入了一种改进的红蓝优化算法 (SWRBMO),以提高收精度并避免局部优化. 在基准测试和工程应用中,SWRBMO表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式计算 超启发式计算
背景情况:
- 红蓝优化算法 (RBMO) 面临的挑战是趋同精度,人口多样性和局部优化.
- 现有的优化算法往往难以有效地平衡探索和开发.
研究的目的:
- 为了提出一种新的多策略,改进了红蓝优化算法 (SWRBMO).
- 为了提高全球勘探,融合速度,局部最佳避免和RBMO算法的准确性.
- 验证SWRBMO在基准函数和工程设计问题上的有效性.
主要方法:
- 实施了基于T分布的适应性 sinh-cosh搜索策略,以改善全球勘探和融合.
- 引入了社区指导的增强策略,以减轻当地最佳情况.
- 纳入一个交叉策略,以提高收准确度.
- 在CEC2005,CEC2019,CEC2021基准套件上对SWRBMO进行评估,包括废除研究.
- 使用威尔科克森等级总和测试进行统计分析,并对工程设计问题进行验证.
主要成果:
- 与RBMO和其他算法相比,SWRBMO在CEC2019和CEC2021测试套件上表现出更快的融合和更高的准确性.
- 废弃性研究证实了每个建议策略的贡献.
- 在工程设计问题上,SWRBMO取得了显著的性能改进,其性能远远超过其他方法 (高达近100%).
结论:
- 拟议的SWRBMO有效地解决了原来的RBMO算法的局限性.
- SWRBMO展示了强大的性能,改进的准确性和实际工程应用的强大潜力.
- 多策略方法增强了优化算法的理论基础和实际实用性.
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