An Improved Particle Swarm Optimization Based on Repulsion Factor

DOI: 10.4236/ojapps.2012.24B027   PDF   HTML     3,034 Downloads   4,612 Views   Citations


In this paper, through the research of advantages and disadvantages of the particle swarm optimization algorithm, we get a new improved particle swarm optimization algorithm based on repulsion radius and repulsive factor. And a lot of test function experimental results show that the algorithm can effectively overcome the PSO algorithm precocious defect. PSO has significant improvement.

Share and Cite:

Zhang, J. , Fan, C. , Liu, B. and Shi, F. (2012) An Improved Particle Swarm Optimization Based on Repulsion Factor. Open Journal of Applied Sciences, 2, 112-115. doi: 10.4236/ojapps.2012.24B027.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Kennedy,Eberhart RC.Particle swarm optimization[C].Proc of the IEEE International Conference on Neural Networks Piscataway.NJ:IEEE Service Center,1995:1942-1948.
[2] Y. Shi and RC.Eberhart. Parameter selection in particle swarm optimization[C]. Evolutionary ProgramminⅦ, V.W. Porto, N.Saravanan, D,Waagen, and A.E.Eiben, Eds. Berlin Germany: Spring-Verlag, 1997:591-600.
[3] Shi Yuhui,Eberhart RC.A modified particle swarm optimizer[C] Proc of the TEEE International Conference on Evolutionary Computation.Piscataway,NJ:IEEE Service Center,1998:69-73.
[4] R. C. Eberhart and Y. Shi. Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization. IEEE,2000:84-88.
[5] R. C. Eberhart and Y. Shi. Fuzzy Adaptive Particle Swarm Optimization. IEEE,2001:101-106.
[6] Krink T., Vesterstrom J.S., Riget J.. Particle swarm optimisation with spatial particle extension[C] Congress on Evolutionary Computation, 2002. CEC '02. 2002 : 1474 - 1479
[7] Clerc M,Kennedy J.Tlle particle swarm-explosions, starbility and convergent in a multidimensional complex space [J] IEEE.Transaction Evolutionary Computation,2002,6(2):58-73.
[8] Ranlaweera A,Halgamuge S K, Watson HC.Self-organizing hierarchical panicle swarm optimizer with time-varying acceleration coefficients[C].IEEE Trans Evol Comput,2004:240-255.
[9] Sedlaczek K, Eberhard P. Using augmented Lagrangian particle swarm optimization for constrained problems in engineering.[J]Struct Multidiscip Optim2006,32(4)277-286.
[10] Zhu Xiaoliu, Xiong Weili, Xu Baoguo. QDPSO Algorithm Based on Simulated Annealing Technique [C]. Computer Engineering,Vol.33 No.15,2007.8:209-210.
[11] Pant M., Radha T., Singh V.P. A Simple Diversity Guided Particle Swarm Optimization. Congress on Evolutionary Computation, 2007. CEC 2007. IEEE. 2007 : 3294 – 3299.
[12] Bi Xiaojun, Liu Guoan. An improved particle swarm optimization algorithm based on population classification[J]. Journal of Harbin Engineering University,2008,29(9):991-996.
[13] Zhang J,Shi Y,zhan ZH.Power electronic circuits design:A particle swarm optimization approach[C].SEAL,2008:605-614.
[14] Mingquan Chen. Second Generation Particle Swarm Optimization. Congress on Evolutionary Computation,IEEE,2008:90-96.
[15] Jakob V. and Rene Thomsen. A Comparative Study of Differential Evolution,Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems[C]. IEEE,2004,1980-1987.
[16] Eberhard P, Sedlaczek K. Using augmented Lagrangian particle swarm opti- mization for constrained problems in engineering[C]. Advanced Design of Mechanical Systems: From Analysis to Optimization. 2009:253–271.
[17] Sun C, Zeng J, Pan J. An improved particle swarm optimization with feasibility- based rules for constrained optimization problems[C]. Next-Generation Applied Intelligence 2009:202–211.
[18] Lingfeng Wang and Chanan Singh. Multicriteria Design of Hybrid Power Generation Systems Based on a Modified Particle Swarm Optimization Algorithm. Transactions on Energy Conversion, IEEE, 2009.3:163-172.
[19] Venter G, Haftka R. Constrained particle swarm optimization using a bi-objective formulation. Struct Multidiscip Optim 2010;40:65-76

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.