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International Journal of Machine Learning and Cybernetics

Erschienen in:

30.11.2018 | Original Article

Bat algorithm with principal component analysis

verfasst von: Zhihua Cui, Feixiang Li, Wensheng Zhang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2019

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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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Titel: Bat algorithm with principal component analysis
verfasst von: Zhihua Cui
Feixiang Li
Wensheng Zhang
Publikationsdatum: 30.11.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Machine Learning and Cybernetics / Ausgabe 3/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI: https://doi.org/10.1007/s13042-018-0888-4

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