Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 10, Issue 3, Pages 603-622Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-018-0888-4
Keywords
Bat algorithm; Principal component analysis; Golden section method
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Funding
- 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]
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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.
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