Journal of Electrical and Electronic Engineering

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Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode

Received: 11 April 2017    Accepted:     Published: 12 April 2017
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Abstract

At the late evolution stage of the basic particle swarm optimization (BPSO), convergence process starts to slow down and the best fitness particle fluctuates around the globally-optimal solution, which may give rise to decrease on convergence precision of the BPSO. Therefore, an improved algorithm for particle swarm optimization was proposed. The modified version of PSO uses a controllable velocity-updating mode to control velocity of evolved particles, which is expected to be useful for tuning the search for the globally-optimal solution. Optimization examples showed that the improved PSO is superior to the BPSO, on not only convergence precision but also computation expense.

DOI 10.11648/j.jeee.20170502.17
Published in Journal of Electrical and Electronic Engineering (Volume 5, Issue 2, April 2017)
Page(s) 68-73
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Particle Swarm Optimization (PSO), Controllable Velocity-Updating Mode, Velocity-Changing Track

References
[1] Kennedy J, Eberhart R C. Particle swarm optimization. In Proc. of the IEEE Conf. on Neural Networks IV, Perth, IEEE Press, 1995, pp. 1942-1948.
[2] Shi Y, Eberhart R C. A Modified Swarm Optimizer. IEEE International Conference of Evolutionary computation, Anchorage, Alaska, 1998, pp. 69-73.
[3] Clerc M. The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization. In Proc. of the ICEC. Washington, 1999, pp. 1951-1957.
[4] Shi Y, Eberhart R C. Fuzzy adaptive particle swarm optimization. In Proc. of the Congress on Evolutionary Computation, Piscataway, IEEE, 2001, pp. 101-106.
[5] Huang Y, Liu Y F, Peng Z M, et al. Research on particle swarm optimization algorithm with characteristic of quantum parallel and its application in parameter estimation for fractional-order chaotic systems. Acta Phys. Sin., 2015, 64 (3): 1-8.
[6] Guo W H, Wang T S. Pre-Impact Configuration Optimization for a Space Robot Capturing Target Satellite. Journal of Astronautics, 2015, 36 (4): 390-396.
[7] Zhai T T, Zhu J Q. New Method for First-Order Structure Design of Continuous Zoom Lens System. Acta Opt. Sin., 2015, 35 (7): 1-9.
[8] Ireneusz G. A new approach to particle swarm optimization algorithm. Expert Systems with Applications, 2015, 42: 844-854.
[9] Cheung N J, Ding X M, Shen H B. A supervised particle swarm algorithm for real-parameter optimization. Application Intelligence, 2015, 43: 825-839.
[10] Tang R L, Fang Y J. Modification of particle swarm optimization with human simulated property. Neurocomputing, 2015, 153: 319-331.
[11] Clerc M, Kennedy J. The particle swarm: Explosion stability and convergence in a multi-dimensional complex space. IEEE Trans. on Evolution Computer, 2002, 6 (1): 58-73.
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  • APA Style

    Jiao Weidong, Huang Zhijing, Yan Gongbiao. (2017). Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode. Journal of Electrical and Electronic Engineering, 5(2), 68-73. https://doi.org/10.11648/j.jeee.20170502.17

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    ACS Style

    Jiao Weidong; Huang Zhijing; Yan Gongbiao. Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode. J. Electr. Electron. Eng. 2017, 5(2), 68-73. doi: 10.11648/j.jeee.20170502.17

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    AMA Style

    Jiao Weidong, Huang Zhijing, Yan Gongbiao. Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode. J Electr Electron Eng. 2017;5(2):68-73. doi: 10.11648/j.jeee.20170502.17

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  • @article{10.11648/j.jeee.20170502.17,
      author = {Jiao Weidong and Huang Zhijing and Yan Gongbiao},
      title = {Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {5},
      number = {2},
      pages = {68-73},
      doi = {10.11648/j.jeee.20170502.17},
      url = {https://doi.org/10.11648/j.jeee.20170502.17},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20170502.17},
      abstract = {At the late evolution stage of the basic particle swarm optimization (BPSO), convergence process starts to slow down and the best fitness particle fluctuates around the globally-optimal solution, which may give rise to decrease on convergence precision of the BPSO. Therefore, an improved algorithm for particle swarm optimization was proposed. The modified version of PSO uses a controllable velocity-updating mode to control velocity of evolved particles, which is expected to be useful for tuning the search for the globally-optimal solution. Optimization examples showed that the improved PSO is superior to the BPSO, on not only convergence precision but also computation expense.},
     year = {2017}
    }
    

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    T1  - Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode
    AU  - Jiao Weidong
    AU  - Huang Zhijing
    AU  - Yan Gongbiao
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    DO  - 10.11648/j.jeee.20170502.17
    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
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    EP  - 73
    PB  - Science Publishing Group
    SN  - 2329-1605
    UR  - https://doi.org/10.11648/j.jeee.20170502.17
    AB  - At the late evolution stage of the basic particle swarm optimization (BPSO), convergence process starts to slow down and the best fitness particle fluctuates around the globally-optimal solution, which may give rise to decrease on convergence precision of the BPSO. Therefore, an improved algorithm for particle swarm optimization was proposed. The modified version of PSO uses a controllable velocity-updating mode to control velocity of evolved particles, which is expected to be useful for tuning the search for the globally-optimal solution. Optimization examples showed that the improved PSO is superior to the BPSO, on not only convergence precision but also computation expense.
    VL  - 5
    IS  - 2
    ER  - 

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Author Information
  • School of Engineering, Zhejiang Normal University, Jinhua, China

  • School of Engineering, Zhejiang Normal University, Jinhua, China

  • Department of Mechanical Engineering, Zhejiang University, Hangzhou, China

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