期刊
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
卷 10, 期 3, 页码 603-622出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-018-0888-4
关键词
Bat algorithm; Principal component analysis; Golden section method
资金
- National Natural Science Foundation of China [61806138, U1636220]
- Natural Science Foundation of Shanxi Province [201601D011045]
- PhD Research Startup Foundation of Taiyuan University of Science and Technology [20182002]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据