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A novel hybrid multi-objective bacterial colony chemotaxis algorithm

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Abstract

In this article, a novel hybrid multi-objective bacterial colony chemotaxis (HMOBCC) algorithm is proposed to solve multi-objective optimization problems. A mechanism of particle swarm optimization is introduced to multi-objective bacterial colony chemotaxis (MOBCC) algorithm to improve the performance of MOBCC algorithm. Also, three other techniques, including dynamic reverse learning operator, external archive multiplying operator and adaptive diversity maintenance operator, are further applied to improve the diversity and convergence of the algorithm. The proposed algorithm is validated using 12 benchmark problems, and three performance measures are implemented for 5 benchmark problems to compare its performance with existing popular algorithms such as MOBCC, multi-objective bacterial colony chemotaxis based on grid algorithm, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective evolutionary algorithm based on decomposition. The results show that the proposed HMOBCC is very effective against existing algorithms.

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Acknowledgements

The authors would like to thank the editor and reviewers for their valuable comments which significantly improve the quality of this paper. At the same time, we would like to thank Xueping Li for his comments on first draft of this paper. This work is supported by National Natural Science Foundation of China (No. 61873225 and No. 61374098) and Natural Science Foundation of Hebei Province Beijing Tianjin Hebei cooperation project (No. F2016203507).

Funding

This work is funded by National Natural Science Foundation of China (No. 61873225 and No. 61374098) and Natural Science Foundation of Hebei Province Beijing Tianjin Hebei cooperation project (No. F2016203507).

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Authors and Affiliations

  1. Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Zhigang Lu, Lijun Geng, Weitao Yao, Guoqiang Li & Xiaoqiang Guo

  2. School of Electrical and Data Engineering, University of Technology Sydney, Sydney, Australia

    Jiangfeng Zhang

  3. China Energy Engineering Group Tianjin Electric Power Design Institute Co., Ltd., Tianjin, 300400, China

    Hao Zhao

  4. State Grid Tianjin Maintenance Company, Tianjin, 300000, China

    Guanghao Huo

Authors
  1. Zhigang Lu
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  2. Lijun Geng
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  3. Guanghao Huo
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  4. Hao Zhao
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  5. Weitao Yao
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  6. Guoqiang Li
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  7. Xiaoqiang Guo
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  8. Jiangfeng Zhang
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Corresponding author

Correspondence to Zhigang Lu.

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All of the author declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Lu, Z., Geng, L., Huo, G. et al. A novel hybrid multi-objective bacterial colony chemotaxis algorithm. Soft Comput 24, 2013–2032 (2020). https://doi.org/10.1007/s00500-019-04034-y

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  • DOI: https://doi.org/10.1007/s00500-019-04034-y

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