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An ensemble bat algorithm for large-scale optimization

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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.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 61806138, Natural Science Foundation of Shanxi Province under Grant No. 201801D121127, Taiyuan University of Science and Technology Scientific Research Initial Funding under Grant No. 20182002.

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Authors and Affiliations

  1. Complex System and Computational Intelligence Laboratory, TaiYuan University of Science and Technology, Taiyuan, 030024, China

    Xingjuan Cai, Jiangjiang Zhang & Hao Liang

  2. Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China

    Lei Wang & Qidi Wu

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  1. Xingjuan Cai
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Correspondence to Xingjuan Cai.

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Cai, X., Zhang, J., Liang, H. et al. An ensemble bat algorithm for large-scale optimization. Int. J. Mach. Learn. & Cyber. 10, 3099–3113 (2019). https://doi.org/10.1007/s13042-019-01002-8

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