基于三角形的正弦共弦算法用于全球搜索和特征选择.
Jiacong Liu1, Chunguang Bi2, Huiling Chen3
1College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
Scientific reports
|April 15, 2025
概括
一个新的优化算法,TTOSCA,通过三角优化和盗窃机制增强了正弦共弦算法 (SCA). TTOSCA及其二进制变体BTTOSCA在复杂的优化和特征选择任务中显示出更高的精度和融合.
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:
- 弦坐标算法 (SCA) 是一个基于人口的元启发式算法.
- 在平衡勘探和开采方面,SCA面临着挑战,特别是在高维空间,导致缓慢的融合和精度降低.
研究的目的:
- 引入一种具有改进的优化能力的新型增强的正弦共弦算法 (TTOSCA).
- 开发和评估二进制变体 (BTTOSCA) 用于离散优化问题,如特征选择.
主要方法:
- 通过将三角优化 (TO) 策略和盗窃机制 (TM) 整合到SCA中,提出了TTOSCA.
- 使用威尔科克森签名等级测试对IEEE CEC2017基准函数的27个算法进行了TTOSCA评估.
- 评估了BTTOSCA对17个UCI数据集进行特征选择,包括医学和基因数据.
主要成果:
- 与现有的算法相比,TTOSCA在精度和合速度方面取得了显著的改进.
- 在特征选择中,BTTOSCA有效地平衡了勘探和开发.
- 在不牺牲分类准确性的情况下,BTTOSCA实现了更小的特征子集.
结论:
- 为了持续优化,TTOSCA为SCA提供了强大的增强功能.
- BTTOSCA是一个强大而高效的工具,用于在高维和离散领域的特征选择.
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