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A General Swarm Intelligence Model for Continuous Function Optimization

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Simulated Evolution and Learning (SEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

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Abstract

We consider a general form of the swarm intelligence as a function optimization tool. This form is derived from a basis of mathematical swarming differential equation model, where several parameters are included in the model. These parameters are corresponding to a repulsion effect, an attractive effect and a gradient direction. We mainly consider a repulsion effect and unknown gradient estimation in this study. The nature of the proposed model by some typical numerical simulation results is described. Then, the numerous simulation results show that the behaviors of the swarm will change significantly, for example, aggregation and clustering by parameter setting. We are able to see basic behaviors of the swarm intelligence by the introduced model, the model could give us the insight to understand search behavior of swarm intelligence.

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Acknowledgement

Satoru Iwasaki and Heng Xiao are supported by JPSS program for Leading Graduate Schools, and a part of this study is supported by JPSS KAKENHI Grant number 15K00338.

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Authors and Affiliations

  1. Department of Information and Physical Sciences, Osaka University, Suita, Japan

    Satoru Iwasaki, Heng Xiao & Toshiharu Hatanaka

  2. Research Institute for Economics and Business Administration, Kobe University, Kobe, Japan

    Takeshi Uchitane

Authors
  1. Satoru Iwasaki
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  2. Heng Xiao
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  3. Toshiharu Hatanaka
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  4. Takeshi Uchitane
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Corresponding author

Correspondence to Toshiharu Hatanaka .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

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Iwasaki, S., Xiao, H., Hatanaka, T., Uchitane, T. (2017). A General Swarm Intelligence Model for Continuous Function Optimization. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_80

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  • DOI: https://doi.org/10.1007/978-3-319-68759-9_80

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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