增强极光优化与加密生物和差异演化为全球优化和特征选择优化
1School of Petroleum Engineering, Yangtze University, Wuhan 430100, China.
Biomimetics (Basel, Switzerland)
|January 24, 2025
概括
新的加密生物和差异进化 (CPLODE) 算法增强了全球优化和特征选择. 在基准函数和现实数据集上,CPLODE表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 优化算法对于解决复杂问题至关重要.
- 功能选择 (FS) 是机器学习中的一个关键的预处理步骤.
研究的目的:
- 引入一个改进的优化算法,CPLODE,增强全球优化和功能选择.
- 将加密生物和差异进化 (DE) 集成到极光优化 (PLO) 算法.
主要方法:
- 取代了PLO的粒子碰撞与DE的突变和交叉操作员.
- 纳入了动态交叉率,以改善收.
- 实施了加密生物体机制,以重复使用成功的解决方案.
主要成果:
- 与其他八种算法相比,CPLODE在29个CEC 2017基准函数上表现出卓越的表现.
- 实现了更高的平均等级和更快的融合率.
- 展示了十个现实数据集的特征选择竞争结果,优于现有算法.
结论:
- CPLODE 是一个有效的算法,用于全球优化和功能选择.
- 加密生物和DE的整合显著提高了优化能力.
- CPLODE提供了更好的分类准确性和特征减少.
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