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A Modified Bacterial Foraging Optimizer with Adaptive Chemotactic Step in Dynamic Search Region

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Intelligent Computing Theories and Application (ICIC 2020)

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Abstract

Bacterial foraging optimization (BFO), inspired from the foraging process of bacterium called E.coli, has been applied successfully to a variety of real world optimization problems. However, BFO easily encounters the issue of poor convergence when dealing with complex landscapes of optimization problems due to its inherent fixed chemotactic strategy. Aiming at the above issue, an adaptive bacterial foraging optimizer is presented in this paper, which is able to obtain a good balance between exploration and exploitation during the search. In this approach, the chemotactic step-length is adjusted dynamically, that is a larger chemotactic step is for global search and a smaller chemotactic step is conducive to local search. Moreover, the outstanding swarming pattern is incorporated to perform information sharing in population during the evolution, aiming to maintain diversity and convergence. Simulation results on a set of benchmark functions validate the effectiveness of the proposed algorithm.

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Acknowledgment

This work was supported in part by National Natural Science Foundation of China under Grant No. 61773103 and Huawei HIRP project under No. HO2019085002.

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Authors and Affiliations

  1. College of Software, Northeastern University, Shenyang, China

    Yibo Yong & Lianbo Ma

  2. 2012 Laboratories, Huawei Technologies Co., Ltd., Shenzhen, China

    Junfeng Zhao & Xiaolong Shen

Authors
  1. Yibo Yong
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  2. Lianbo Ma
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  3. Junfeng Zhao
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  4. Xiaolong Shen
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Corresponding author

Correspondence to Lianbo Ma .

Editor information

Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Yong, Y., Ma, L., Zhao, J., Shen, X. (2020). A Modified Bacterial Foraging Optimizer with Adaptive Chemotactic Step in Dynamic Search Region. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_4

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  • DOI: https://doi.org/10.1007/978-3-030-60799-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60798-2

  • Online ISBN: 978-3-030-60799-9

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