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Hybrid Bacterial Forging Optimization Based on Artificial Fish Swarm Algorithm and Gaussian Disturbance

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

Abstract

Traditional Bacterial Forging Optimization (BFO) has poor convergence speed and is easily trapped in the local optimum while dealing with some complex problems. Facing these disadvantages, a new hybrid algorithm for BFO based on Artificial Fish Swarm Algorithm (AFSA) and Gaussian disturbance is proposed, abbreviated as AF-GBFO. The algorithm combines following and swarming behaviors in AFSA with the chemotaxis part of BFO so that bacteria can update positions by evaluating the value of their own and others positions. The convergence speed can be improved in this way. The algorithm also combines Gaussian disturbance to change bacteria’s positions by adding a number following Gaussian distribution. In that case, if all bacteria gather around the local optimum, they still have chance to get out of it. Meanwhile the elimination-dispersal way has been changed to have half of the bacteria eliminated and keep the positions with good values so that the convergence speed is increased. Compared with original BFO, GA, BFOLIW and BFONIW, AF-GBFO outperforms in most cases especially for the multimodal functions.

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Acknowledgment

This work is partially supported by the Natural Science Foundation of Guangdong Province (2018A030310575), Natural Science Foundation of Shenzhen University (8530 3/00000155), Project supported by Innovation and Entrepreneurship Research Center of Guangdong University Student (2018A073825), Research Cultivation Project from Shenzhen Institute of Information Technology (ZY201717) and Innovating and Upgrading Institute Project from Department of Education of Guangdong Province (2017GWT SCX038). Ruozhen Zheng and Zhiqin Feng are first authors. They contributed equally to this paper.

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Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, China

    Ruozhen Zheng & Zhiqin Feng

  2. College of Economy, Shenzhen University, Shenzhen, China

    Jiaqi Shi

  3. College of Software, Shenzhen University, Shenzhen, China

    Shukun Jiang

  4. College of Management, Shenzhen Institute of Information Technology, Shenzhen, China

    Lijing Tan

Authors
  1. Ruozhen Zheng
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  2. Zhiqin Feng
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  3. Jiaqi Shi
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  4. Shukun Jiang
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  5. Lijing Tan
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Corresponding authors

Correspondence to Jiaqi Shi or Shukun Jiang .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Zheng, R., Feng, Z., Shi, J., Jiang, S., Tan, L. (2020). Hybrid Bacterial Forging Optimization Based on Artificial Fish Swarm Algorithm and Gaussian Disturbance. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_11

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  • DOI: https://doi.org/10.1007/978-981-15-3425-6_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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