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An improved double-population artificial bee colony algorithm based on heterogeneous comprehensive learning

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Abstract

The artificial bee colony (ABC) algorithm is one of the popular swarm intelligence algorithms that is inspired by the forging behavior of honeybee colonies. To improve the convergence precision of the ABC algorithm, accelerate the search speed of finding the best solution and control the balance between exploration and exploitation, we propose an improved double-population ABC algorithm based on heterogeneous comprehensive learning (HCLIABC). In this algorithm, the swarm is divided into exploration-subpopulation named group 1 and exploitation-subpopulation named group 2. Illuminated by particle swarm optimization (PSO), the food source will be updated on all dimensions rather than on a randomly selected dimension. Meanwhile HCL strategy is used to generate the exemplars for two subpopulations. In addition, opposition-based learning is used to improve the quality of initial swarm, and multiplicative weight update method is used to update the selection probability of the double-population in employed bees phase. To evaluate the remarkable performance of the improved algorithm, we conduct comparative experiments of 18 unimodal, multimodal, and rotated benchmark functions on dimensions 30 and 100. Computational results demonstrate that HCLIABC can effectively prevent premature convergence and produce competitive optimization precision and convergence speed compared with several popular and classic DE, PSO and ABC variants.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 11431002, 11371197), a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and the Foundation of Jiangsu Key Lab for NSLSCS (Grant No. 201601). The authors thank the anonymous reviewers for providing valuable comments to improve this paper and add special thanks to Professor Suganthan PN for providing the source codes of the comparative algorithms (HCLPSO). The authors also thank Professor Wu Guohua for providing the excellent simulation results of jDE and JADE on 30D and 100D of the 18 benchmark functions.

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Authors and Affiliations

  1. School of Mathematical Sciences, Nanjing Normal University, Nanjing, 210023, People’s Republic of China

    Jiacui Wang, Yuehong Sun & Foxiang Liu

  2. Jiangsu Provincial Key Laboratory for Numerical Simulation of Large Scale Complex Systems, Nanjing, 210023, People’s Republic of China

    Yuehong Sun

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  1. Jiacui Wang
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  2. Yuehong Sun
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  3. Foxiang Liu
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Correspondence to Yuehong Sun.

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No conflict of interest exits in the submission of this article, and it is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.

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Communicated by V. Loia.

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Wang, J., Sun, Y. & Liu, F. An improved double-population artificial bee colony algorithm based on heterogeneous comprehensive learning. Soft Comput 22, 6489–6514 (2018). https://doi.org/10.1007/s00500-017-2700-x

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