EODE-PFA:用于工程优化和特征选择的多策略增强的路径查找算法
Meiyan Li1, Chuxin Cao2, Mingyang Du3
1School of Science, Hainan University, Haikou 570100, China.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
本研究介绍了一种增强的Pathfinder算法 (EODE-PFA),以改善群集智能优化. 新的算法平衡了探索和利用,在基准函数和现实世界的工程和特征选择问题上表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 群体情报优化 群体情报优化
- 超启发式算法 超启发式算法
背景情况:
- 原始的Pathfinder算法 (PFA) 具有不平衡的优化能力,导致人口多样性较低和趋同缓慢.
- 现有的PFA限制阻碍了有效的全球勘探和当地开发.
研究的目的:
- 通过使用多策略改进,提出一个增强的Pathfinder算法 (EODE-PFA).
- 在群集智能算法中解决全球探索和本地优化之间的平衡问题.
主要方法:
- 开发了基于多策略 (EODE-PFA) 的增强路径查询算法.
- 在CEC2022基准函数,复杂的工程问题和特征选择任务上验证了EODE-PFA.
- 将EODE-PFA与八个已建立的优化算法进行比较,并进行了废弃实验.
主要成果:
- 在各种场景中,EODE-PFA在融合速度和解决方案准确性方面表现出显著的优势.
- 废弃性研究证实了实施的战略的协同效益.
- 使用威尔科克森签名等级测试的统计分析验证了结果的意义.
结论:
- EODE-PFA有效地平衡了勘探和开采,超过了现有的算法.
- 拟议的算法在各种优化场景中表现出强大的工程实用性和通用性,包括离散特征选择.
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