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A hybrid approach to artificial bee colony algorithm

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Abstract

In this paper, we put forward a hybrid approach based on the life cycle for the artificial bee colony algorithm to generate dynamical varying population as well as ensure appropriate balance between exploration and exploitation. The bee life-cycle model is firstly constructed, which means that each individual can reproduce or die dynamically throughout the searching process and population size can dynamically vary during execution. With the comprehensive learning, the bees incorporate the information of global best solution into the search equation for exploration, while the Powell’s search enables the bees deeply to exploit around the promising area. Finally, we instantiate a hybrid artificial bee colony (HABC) optimizer based on the proposed model, namely HABC. Comprehensive test experiments based on the well-known CEC 2014 benchmarks have been carried out to compare the performance of HABC against other bio-mimetic algorithms. Our numerical results prove the effectiveness of the proposed hybridization scheme and demonstrate the performance superiority of the proposed algorithm.

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Acknowledgments

This research is partially supported by National Natural Science Foundation of China and Grants 51205389, and 71271140; the National High Technology Research and Development Program of China (863 Program) (No. 2014AA052101-3).

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Authors and Affiliations

  1. Laboratory of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Lianbo Ma, Yunlong Zhu & Dingyi Zhang

  2. Key Laboratory of Networked Control System CAS, Shenyang, 110016, China

    Lianbo Ma

  3. College of Management, Shenzhen University, Shenzhen, 518060, China

    Ben Niu

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  1. Lianbo Ma
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  2. Yunlong Zhu
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  3. Dingyi Zhang
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  4. Ben Niu
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Correspondence to Lianbo Ma.

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Ma, L., Zhu, Y., Zhang, D. et al. A hybrid approach to artificial bee colony algorithm. Neural Comput & Applic 27, 387–409 (2016). https://doi.org/10.1007/s00521-015-1851-x

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