三种策略增强了生物科蒂优化算法,用于全球优化和特征选择问题
Qingzheng Cao1, Shuqi Yuan2, Yi Fang3
1School of Mechanical Engineering, Hunan Institute of Science and Technology, Yueyang 414006, China.
Biomimetics (Basel, Switzerland)
|June 25, 2025
概括
生物ABCCOA算法有效地消除了大型数据集中的冗余特征,提高了模型训练效率和分类准确性. 这种新的方法增强了全球搜索和本地利用,以进行强大的特征选择.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 工业数字化依赖于大型数据集进行模型训练,但冗余的功能增加了计算成本并减少了概括性.
- 现有的优化算法在功能选择 (FS) 挑战中扎,例如不充分的全球搜索和次优化解决方案.
研究的目的:
- 提出生物ABCCOA算法,用于增强数据集中的冗余特征消除.
- 为了提高FS问题的Coati优化算法 (COA) 的全球搜索性能和分类准确性.
主要方法:
- 引入了结合个人学习和差异学习能力的适应性搜索策略,以加强全球探索.
- 开发了一个具有相位控制和动态调节的平衡因子,以平衡勘探和开采,避免次优子集.
- 实施了一个中心点指导策略,其中包括人口中心点指导和分数顺序的历史记忆,以改善本地利用并减少分类错误.
主要成果:
- 生物ABCCOA算法显示了超过90%的优化成功率和测试函数和工程问题更快的融合.
- 在关键指标中的27个FS问题中表现优于比较算法:健身值,分类准确性,子集大小和运行时间.
结论:
- 生物ABCCOA算法是功能选择的高效和强大的解决方案,显著改善了工业数字化数据集预处理.
- 这些改进解决了原始COA的关键限制,在复杂的FS任务中提供了卓越的性能.
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