OJAppS  Vol.2 No.4 B , December 2012
An Improved Particle Swarm Optimization Based on Repulsion Factor
ABSTRACT
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.

Cite this paper
nullZhang, 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.
References
[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

 
 
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