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Surrogate-Assisted Artificial Bee Colony Algorithm

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Part of the book series:Communications in Computer and Information Science ((CCIS,volume 1565))

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Abstract

Search strategies play an essential role in the artificial bee colony (ABC) algorithm. Different optimization problems and search stages may need different search strategies. However, it is not easy to choose an appropriate search strategy efficiently. In order to select an appropriate search strategy with few evaluations, this paper proposes a surrogate-assisted ABC (called SAABC). Based on our previous work, we construct a strategy pool that contains three search strategies. Then, the radial basis function (RBF) network is applied to evaluate the offspring generated by the search strategies. The search strategy with the best evaluation value will be used to guide the population. A set of 22 classical benchmark problems with 30 dimensions are utilized to verify the performance of SAABC. Experimental results show that SAABC achieves better performance than five other ABC algorithms.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 62166027), and Jiangxi Provincial Natural Science Foundation (Nos. 20212ACB212004 and 20212BAB202023).

Author information

Authors and Affiliations

  1. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Tao Zeng, Hui Wang, Tingyu Ye & Luqi Zhang

  2. School of Business Administration, Nanchang Institute of Technology, Nanchang, 330099, China

    Wenjun Wang

Authors
  1. Tao Zeng

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  2. Hui Wang

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  3. Wenjun Wang

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  4. Tingyu Ye

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  5. Luqi Zhang

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Corresponding author

Correspondence toHui Wang.

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Taiyuan University of Science and Technology, Taiyuan, China

    Zhihua Cui

  3. Taiyuan University of Science and Technology, Taiyuan, China

    Jianghui Cai

  4. Huazhong University of Science and Technology, Wuhan, China

    Lianghao Li

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Zeng, T., Wang, H., Wang, W., Ye, T., Zhang, L. (2022). Surrogate-Assisted Artificial Bee Colony Algorithm. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1565. Springer, Singapore. https://doi.org/10.1007/978-981-19-1256-6_19

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