Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode

Jiao Weidong; Huang Zhijing; Yan Gongbiao

doi:doi:10.11648/j.jeee.20170502.17

| Peer-Reviewed

Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode

Jiao Weidong, Huang Zhijing, Yan Gongbiao

Published in Journal of Electrical and Electronic Engineering (Volume 5, Issue 2)

Received: 11 April 2017 Published: 12 April 2017

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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.

Published in	Journal of Electrical and Electronic Engineering (Volume 5, Issue 2)
DOI	10.11648/j.jeee.20170502.17
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), 2017. 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.
[12]	Marini F, Walczak B. Particle swarm optimization (PSO): A tutorial. Chemometrics and Intelligent Laboratory Systems, 2015, 149: 153-165.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode
AU  - Jiao Weidong
AU  - Huang Zhijing
AU  - Yan Gongbiao
Y1  - 2017/04/12
PY  - 2017
N1  - https://doi.org/10.11648/j.jeee.20170502.17
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
SP  - 68
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  -

Copy | Download

Author Information

Jiao Weidong

School of Engineering, Zhejiang Normal University, Jinhua, China
Huang Zhijing

School of Engineering, Zhejiang Normal University, Jinhua, China
Yan Gongbiao

Department of Mechanical Engineering, Zhejiang University, Hangzhou, China

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode
AU  - Jiao Weidong
AU  - Huang Zhijing
AU  - Yan Gongbiao
Y1  - 2017/04/12
PY  - 2017
N1  - https://doi.org/10.11648/j.jeee.20170502.17
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
SP  - 68
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  -

Copy | Download