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New Particle Swarm Optimization Based on Sub-groups Mutation

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Intelligent Computing Methodologies (ICIC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10956))

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Abstract

In order to overcome the premature convergence of particle swarm optimization (PSO) algorithm, an improved PSO algorithm based on sub-groups mutation (SsMPSO) is proposed. This algorithm has proposed the sub-groups with random directional vibrating search to mutate the global optimal position of the main swarm and changed the way of random mutation. The mutation based on sub-groups enabled the algorithm had excellent local exploit ability and circumvented the premature convergence. It used another mutation on bad particles to enhance the algorithm’s global exploit ability and expand the searching space. Finally, high dimension benchmark functions have been used to test the performance of improved algorithm. The simulation results show that the proposed algorithm can effectively overcome the premature problem, the multimodal function optimization can avoid local extreme point and the convergence and convergence accuracy are greatly improved.

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Acknowledgment

This work was supported in part by Hubei Province Natural Science Foundation of China (No. 2018CFB526), by National Natural Science Foundation of China (No. 61502356).

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Authors and Affiliations

  1. Faculty of Information Engineering, City College Wuhan University of Science and Technology, Wuhan, 430083, China

    Zhou Fengli & Lin Xiaoli

Authors
  1. Zhou Fengli
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  2. Lin Xiaoli
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Corresponding author

Correspondence to Zhou Fengli .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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Fengli, Z., Xiaoli, L. (2018). New Particle Swarm Optimization Based on Sub-groups Mutation. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_66

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  • DOI: https://doi.org/10.1007/978-3-319-95957-3_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95956-6

  • Online ISBN: 978-3-319-95957-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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