一个新的混合改进的RIME算法用于全球优化问题
Wuke Li1, Xiong Yang2, Yuchen Yin3
1School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China.
Biomimetics (Basel, Switzerland)
|January 24, 2025
概括
混合估计Rime-ice优化 (HERIME) 算法通过增强勘探和平衡开发来改善全球问题解决. HERIME在优化任务中表现出卓越的搜索效率和准确性.
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:
- 基于物理的元启发式算法RIME算法在全球优化方面表现出色,但受到有限的探索,缓慢的融合和探索-利用不对称的影响.
- 工程应用通常需要强大的优化方法,能够处理复杂的全球问题.
研究的目的:
- 引入混合估计Rime-ice优化 (HERIME),一个增强的元启发式算法.
- 解决原始RIME算法的局限性,特别是改善勘探,融合速度和勘探与开发之间的平衡.
主要方法:
- 从分布估计算法中纳入基于概率模型的抽样方法,以提高人口质量和全球探索.
- 实施基于轮盘的健身距离平衡的选择策略,以增强硬阶段和平衡优化阶段.
- 使用IEEE CEC2017和IEEE CEC2022测试套件中的41个函数进行验证.
主要成果:
- 在优化准确性,收性和稳定性方面,HERIME显著超过了经典,近期和先进的元启发算法.
- 统计测试 (弗里德曼,威尔科克森等级总和) 证实了HERIME的优越性能.
- 废弃实验验证了 HERIME 中集成的个别策略的有效性.
结论:
- 与现有方法相比,HERIME提供了更好的搜索效率和优化准确性.
- 拟议的算法对于解决工程和其他领域的全球优化问题非常有效.
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