一个新的雪优化高维特征选择问题
Jia Guo1,2,3,4, Wenhao Ye5, Dong Wang6
1Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan 430205, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
一个新的雪优化 (SLO) 算法平衡了复杂问题的探索和利用. 在高维优化和特征选择方面,SLO 卓越,性能优于现有方法.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 生物启发的计算 生物启发的计算
背景情况:
- 传统的优化方法在高维问题上扎,限制了准确性.
- 超启发式算法提供了潜力,但对于复杂的搜索空间需要新的方法.
研究的目的:
- 介绍雪优化 (SLO) 算法,这是一个新的元启发式.
- 评估SLO在解决高维优化和特征选择任务方面的有效性.
主要方法:
- 由雪的领土行为 (划界,迁移,争端机制) 启发的SLO算法.
- 使用CEC2017基准函数进行绩效评估.
- 适用于高维基遗传数据的特征选择.
主要成果:
- SLO在勘探和开采之间取得了平衡.
- 在2017年CEC的弗里德曼测试中,SLO排名第一,表现优于ETBBPSO,ARBBPSO,HCOA,AVOA,WOA,SSA和HHO.
- 在高维基遗传数据特征选择中,SLO显示出了实用效用.
结论:
- SLO是一个具有竞争力和适应性的算法,用于高维优化.
- 该研究标志着高维优化和特征选择方法的重大进展.
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