IRIME:在RIME优化中减轻开发-勘探不平衡,以优化功能选择
Jinpeng Huang1, Yi Chen1, Ali Asghar Heidari2
1Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
iScience
|August 21, 2024
概括
改进的Rime优化算法 (IRIME) 增强了探索,并避免了局部优化. 它的二进制版本,bIRIME,在特征选择方面表现出色,在精度和子集选择方面表现优于其他算法.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 机器学习 机器学习
背景情况:
- 里姆优化算法 (RIME) 面临的挑战包括低于最佳的融合和勘探与开发之间的不平衡.
- 这些局限性阻碍了其在解决复杂优化问题的有效性.
研究的目的:
- 引入一个增强的Rime优化算法 (IRIME),解决原始RIME的局限性.
- 评估IRIME在基准问题上的表现及其对工程和特征选择任务的适用性.
主要方法:
- IRIME集成了软包围 (SB),复合突变策略 (CMS) 和重启策略 (RS).
- 使用IEEE CEC 2017基准测试和四个工程问题来验证性能.
- 一个二进制版本,bIRIME,被开发用于特征选择.
主要成果:
- 在基准测试中,IRIME与其他先进算法相比表现优越.
- IRIME有效地解决了实际的工程问题.
- bIRIME在各种特征选择数据集上取得了出色的结果,在子集选择和分类准确性方面表现优于现有的方法.
结论:
- 与标准的RIME算法相比,IRIME提供了显著的改进.
- bIRIME显示出在机器学习应用中有效选择功能的巨大潜力.
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