Bat algorithm with principal component analysis

被引：83

作者：

Cui, Zhihua ^{[1
]}

Li, Feixiang ^{[1
]}

Zhang, Wensheng ^{[2
]}

机构：

[1] Taiyuan Univ Sci & Technol, Taiyuan 030024, Shanxi, Peoples R China

[2] Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2019年 / 10卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Bat algorithm; Principal component analysis; Golden section method; PARTICLE SWARM OPTIMIZATION; SELECTION; PERFORMANCE; STRATEGY; SYSTEMS;

D O I：

10.1007/s13042-018-0888-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The bat algorithm (BA) is a novel evolutionary optimization algorithm, most studies of which have been performed with low-dimensional problems. To test and improve the global search ability of BA with large-scale problems, two new variants using principal component analysis (PCA_BA and PCA_LBA) are designed in this paper. A correlation threshold and generation threshold are determined using the golden section method to enhance the effectiveness of this new strategy. To test performance, CEC'2008 large-scale benchmark functions are utilized and compared with other algorithms; simulation results indicate the validity of this modification.

引用

页码：603 / 622

页数：20

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