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Bat algorithm with principal component analysis

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Abstract

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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Acknowledgements

This paper was supported by National Natural Science Foundation of China under Grant No. 61806138 and U1636220, Natural Science Foundation of Shanxi Province under Grant No. 201601D011045 and PhD Research Startup Foundation of Taiyuan University of Science and Technology under Grant No. 20182002.

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Authors and Affiliations

  1. Taiyuan University of Science and Technology, Shanxi, 030024, China

    Zhihua Cui & Feixiang Li

  2. Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Wensheng Zhang

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  1. Zhihua Cui
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Cui, Z., Li, F. & Zhang, W. Bat algorithm with principal component analysis. Int. J. Mach. Learn. & Cyber. 10, 603–622 (2019). https://doi.org/10.1007/s13042-018-0888-4

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