混乱的RIME优化算法与适应性互惠主义,用于特征选择问题
Mahmoud Abdel-Salam1, Gang Hu2, Emre Çelik3
1Faculty of Computer and Information Science, Mansoura University, Mansoura, 35516, Egypt.
Computers in biology and medicine
|July 2, 2024
概括
适应性混乱的RIME (ACRIME) 算法通过改善人口多样性和平衡勘探-开采来增强优化. 它在特征选择和分类任务中的表现优于其他方法,包括COVID-19数据分析.
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:
- 像RIME这样的基于群体的优化算法在勘探-开发平衡方面存在局限性,导致局部最佳和缓慢的融合.
- 提高搜索机制对于发现复杂问题的多样化和最佳解决方案至关重要.
研究的目的:
- 引入自适应混沌RIME (ACRIME) 算法,旨在克服原始RIME算法的局限性.
- 改善人口多样性,平衡勘探和开发,增强本地和全球搜索能力.
- 评估ACRIME在基准功能,现实世界特征选择任务和COVID-19分类方面的有效性.
主要方法:
- 通过使用混乱地图进行智能人口初始化,修改了Symbiotic Organism Search (SOS) 互惠阶段,混合突变策略和重启策略,开发了ACRIME.
- 使用CEC2005和CEC2019基准函数进行评估ACRIME.
- 将ACRIME应用于14个数据集,用于特征选择和COVID-19分类数据.
- 使用威尔科克森等级和弗里德曼等级测试,将ACRIME与其他元启发方法进行比较.
主要成果:
- ACRIME在与已建立的算法相比,表现出了卓越的性能和竞争力.
- 该算法有效地识别了最佳特征子集,提高了分类准确性,同时减少了特征数量.
- 统计测试证实了ACRIME的显著性能改善.
结论:
- ACRIME通过改善勘探-开发平衡和搜索范围,成功地提高了RIME算法的性能.
- 拟议的算法显示了现实应用的巨大潜力,特别是在特征选择和分类任务中.
- ACRIME为复杂的优化问题提供了强大而有效的方法.
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