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Artificial bee colony algorithm with an adaptive search manner and dimension perturbation

  • S.I. : NCAA 2021
  • Published:
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Abstract

Artificial bee colony (ABC) can effectively solve some complex optimization problems. However, its convergence speed is slow and the exploitation capacity is insufficient at the last search stage. In order to tackle these issues, this paper proposes a modified ABC with an adaptive search manner and dimension perturbation (called ASDABC). There are two important search manners: exploration and exploitation. A suitable search manner is beneficial for the search. An explorative search strategy and another exploitative search strategy are selected to build a strategy pool. To adaptively choose an appropriate search manner, an evaluating indicator is designed to relate the current search status. According to the evaluating indicator, an adaptive method is used to determine which kind of search manner is suitable for the current search. Additionally, a dynamic dimension perturbation strategy is used to enhance the exploration and exploration ability. To verify the performance of ASDABC, 50 problems are tested including 22 classical functions and 28 complex functions. Experiment result shows that ASDABC achieves competitive performance when contrasted with seven different ABC variants.

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Acknowledgements

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

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Authors and Affiliations

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

    Tingyu Ye, Hui Wang, Tao Zeng, Luqi Zhang & Zhikai Huang

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

    Wengjun Wang

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  1. Tingyu Ye
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  2. Hui Wang
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  4. Tao Zeng
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Correspondence to Hui Wang.

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Ye, T., Wang, H., Wang, W. et al. Artificial bee colony algorithm with an adaptive search manner and dimension perturbation. Neural Comput & Applic 34, 16239–16253 (2022). https://doi.org/10.1007/s00521-022-06981-4

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