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A hybrid artificial bee colony algorithm with modified search model for numerical optimization

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Abstract

Artificial bee colony (ABC) is an effective optimization algorithm, which has been used in various practical applications. However, the standard ABC suffers from low accuracy of solutions and slow convergence rate. To address these issues, a hybrid ABC (called HABC) is proposed in this paper. In HABC, two improved strategies are utilized. First, a new search model is designed based on the best-of-random mutation scheme. Second, new solutions are generated by updating multiple dimensions. To verify the performance of HABC, twelve numerical optimization problems are tested in the experiments. Results of HABC are compared the standard ABC and two other improved ABC versions. The comparison show that our approach can effectively improve the optimization performance.

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Acknowledgements

This work is supported by the project of the First-Class University and the First-Class Discipline(10301-017004011501), and the National Natural Science Foundation of China.

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Authors and Affiliations

  1. School of Information Engineering, Minzu University of China, Beijing, 100081, China

    Xiuqin Pan, Yong Lu, Na Sun & Sumin Li

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  1. Xiuqin Pan
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  2. Yong Lu
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  3. Na Sun
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  4. Sumin Li
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Correspondence to Xiuqin Pan.

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Pan, X., Lu, Y., Sun, N. et al. A hybrid artificial bee colony algorithm with modified search model for numerical optimization. Cluster Comput 22 (Suppl 2), 2581–2588 (2019). https://doi.org/10.1007/s10586-017-1343-0

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  • DOI: https://doi.org/10.1007/s10586-017-1343-0

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