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A novel artificial bee colony clustering algorithm with comprehensive improvement

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Abstract

This paper provides a novel clustering algorithm named CEABC (Comprehensively Enhanced Artificial Bee Colony Algorithm), enhanced by multiple operators including K-means, PSO and directed by Gbest mechanism is proposed, which can be used to solve clustering problem. In the initial stage, the algorithm uses K-means operator to generate the initial nectar source, which not only improves the quality of the initial nectar source, but also avoids the problem of low operating efficiency. In order to enhance the interaction between individuals, the concept of global optimum solution which can also be called Gbest is introduced, where the original one-dimensional information exchange is replaced by the full-dimensional information exchange among nectar sources, so that the information exchange volume of the whole swarm can be improved. In the phase of bee scouting, the above-mentioned global optimal solution is combined with PSO algorithm to generate a brand-new nectar source search method, which will enhance the ability of ABC algorithm to develop nectar sources. Meanwhile, the global optimal solution will continue to guide the bee scouting to generate new nectar sources so as to improve the overall quality of the nectar sources. Then, the experiment carried out on two sets of artificial data sets and four sets of UCI machine learning data sets which are the most representative verifies the clustering performance of the newly proposed CEABC algorithm. In addition, the experimental results are compared with standard ABC algorithm, several other newly proposed ABC-improved algorithms and classical clustering algorithms. Results clearly show that the novel ABC algorithm described in this paper has better accuracy and stability in solving clustering problems, which is a more effective clustering algorithm. We believe that clustering method for high-dimensional data can provide an effective method for data processing in materials, medical treatment, automatic driving and other application fields.

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Acknowledgements

This work was supported in part by The National Nature Science Foundation of China (No. 31971311),in part by The National Social Science Fund of China(NO.20BGL251)

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Authors and Affiliations

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

    Qiumei Pu, Chiquan Xu & Hui Wang

  2. Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China

    Lina Zhao

  3. University of Chinese Academy of Sciences, Beijing, 101408, China

    Lina Zhao

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  1. Qiumei Pu
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Pu, Q., Xu, C., Wang, H. et al. A novel artificial bee colony clustering algorithm with comprehensive improvement. Vis Comput 38, 1395–1410 (2022). https://doi.org/10.1007/s00371-021-02367-0

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