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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 11/2019

26.08.2019 | Original Article

An ensemble bat algorithm for large-scale optimization

verfasst von: Xingjuan Cai, Jiangjiang Zhang, Hao Liang, Lei Wang, Qidi Wu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 11/2019

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Abstract

It is difficult for the bat algorithm (BA) to retain good performance with increasing problem complexity and problem. In this paper, an ensemble BA is proposed to solve large-scale optimization problems (LSOPs) by introducing the integration ideas. The characteristics of six improved BA strategies are taken into account for the ensemble strategies. To fuse these strategies perfectly, the probability selection mechanisms, including the constant probability and dynamic probability, are designed by adjusting the odds of different strategies. To verify the performance of the algorithm in this paper, the proposed algorithm is applied to solve numerical optimization problems on benchmark functions with different dimensions. Then, the best ensemble BA is selected by comparing the constant probabilities and dynamic probabilities. The selected algorithm is compared with other excellent swarm intelligence optimization algorithms. Additionally, the superiority of the proposed algorithm is confirmed for solving LSOPs.
Vorheriger Artikel Classifying imbalanced data using ensemble of reduced kernelized weighted extreme learning machine
Nächster Artikel PKGCN: prior knowledge enhanced graph convolutional network for graph-based semi-supervised learning

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Metadaten
Titel
An ensemble bat algorithm for large-scale optimization
verfasst von
Xingjuan Cai
Jiangjiang Zhang
Hao Liang
Lei Wang
Qidi Wu
Publikationsdatum
26.08.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 11/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-01002-8

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