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An improved global best guided artificial bee colony algorithm for continuous optimization problems

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Abstract

Artificial bee colony (ABC) has proved to be a good optimizer on many engineering problems. However, the standard ABC converges slowly because of poor exploitation. Recently, a new ABC variant called global best guided ABC (namely GABC) was proposed. Some simulation studies proved that GABC could find more accurate solutions than the standard ABC. In this paper, an improved ABC (namely IABC) is proposed to strengthen the performance of GABC. In IABC, the onlooker bees use a modified solution search equation to improve the exploitation capability. Experiment verification is carried out on ten classical benchmark functions. Results show our new approach IABC is superior to the standard ABC, GABC and another improved ABC variant.

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Acknowledgements

This work is supported by the project of the First-Class University and the First-Class Discipline (No. 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

    Yongcun Cao, Yong Lu, Xiuqin Pan & Na Sun

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  1. Yongcun Cao
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  2. Yong Lu
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  3. Xiuqin Pan
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  4. Na Sun
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Correspondence to Yongcun Cao or Xiuqin Pan.

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Cao, Y., Lu, Y., Pan, X. et al. An improved global best guided artificial bee colony algorithm for continuous optimization problems. Cluster Comput 22 (Suppl 2), 3011–3019 (2019). https://doi.org/10.1007/s10586-018-1817-8

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  • DOI: https://doi.org/10.1007/s10586-018-1817-8

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