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一个加速的正弦映射鱼优化器用于特征选择选择

Helong Yu¹, Zisong Zhao¹, Ali Asghar Heidari²

¹College of Information Technology, Jilin Agricultural University, Changchun 130118, China.

|October 20, 2023

概括

一个新的基于弦值的鱼优化算法 (SWEWOA) 通过平衡勘探和开发来提高全球优化. 这种改进的算法在分类任务的特征选择方面表现出色,性能优于现有的方法.

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:

全球优化问题需要高效的算法来探索广的解决方案空间.
标准的鱼优化算法 (WOA) 在平衡勘探和开发方面面临挑战.
提高WOA的性能对于复杂的优化和功能选择任务至关重要.

研究的目的:

为全球优化引入改进的鱼优化算法 (SWEWOA).
使用增强的算法开发一种新的特征选择方法 (BSWEWOA-KELM).
评估SWEWOA和BSWEWOA-KELM在基准数据集上的有效性.

主要方法:

使用正弦映射策略 (SS) 进行人口初始化.
整合逃逸能量 (EE) 以平衡勘探和开采.
整合虫洞搜索 (WS) 以提高开发能力.
开发一个二进制SWEWOA与内核极端学习机器 (KELM) 结合用于特征选择.

主要成果:

在CEC 2017和2022测试集中,SWEWOA在26个比较算法上表现出卓越的性能.
在16个不同的数据集中,BSWEWOA-KELM在特征选择方面超过了8个高性能算法.
混合方法显著增强了优化和功能选择能力.

结论:

拟议的SWEWOA有效地解决了全球优化挑战.
BSWEWOA-KELM为分类问题提供了一种优秀的特征选择方法.
混合策略显著提高了鱼优化算法的性能.

关键词:

计算机科学计算机科学工程工程师工程师工程师自然科学自然科学

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