Improved PSO algorithm and its application

Abstract

The mechanism of particle swarm optimization algorithm is studied, and one can draw the conclusion that the best particle found by the swarm falling into local minima is one of the main reasons for premature convergence. Therefore, an improved particle swarm optimization algorithm is proposed. This algorithm selects the best particle with roulette wheel selection method, so premature converging to local optima is avoided. At last, the improved particle swarm optimization algorithm is applied to optimization of time-sharing power supply for zinc electrolytic process. Simulation and practical results show that the global search ability of IPSO is improved greatly and optimization of time-sharing power supply for zinc electrolytic process can bring about outstanding economic benefit for plant.

Keywords

particle swarm optimization / premature convergence / roulette wheel / zinc electrolytic process / time-sharing power supply

References

[1] Kennedy J, Eberhart R. Particle swarm optimization [A]. Proceedings of IEEE International Conference on Neural Networks [C]. Perth: IEEE, 1995. 1942–1948.

[2] EberhartR, KennedyJ. A new optimizer using particle swarm theory [A]. Proceedings of the 16th International Symposium on Micro Machine and Human Science [C], 1995, Nagoya, Japan, IEEE: 39-43

[3] AbidoM A. Optimal power flow using particle swarm optimization [J]. International Journal of Electrical Power and Energy Systems, 2002, 24(7): 563-571

[4] GaoHai-bing, GaoLiang, ZhouChi, et al.. Particle swarm optimization based algorithm for neural network learning [J]. Acta Electronica Sinica, 2004, 32(9): 1572-2574(in Chinese)

[5] GaingZ L. A particle swarm optimization approach for optimum design of PID controller in AVR system [J]. IEEE Transactions on Energy Conversion, 2004, 19(2): 384-391

[6] KeJing, QianJi-xin. Nonlinear system identification using particle swarm optimization [J]. Journal of Circuits and System, 2003, 8(4): 12-15(in Chinese)

[7] CzarnA, MacNishC, VijayanK, et al.. Statistical exploratory analysis of genetic algorithms [J]. IEEE Transactions on Evolutionary Computation, 2004, 8(4): 405-421

[8] LiNing, LiuFei, SunDe-bao. A study on the particle swarm optimization with mutation operator constrained layout optimization [J]. Chinese Journal of Computers, 2004, 27(7): 897-903(in Chinese)

[9] LüZhen-su, HouZhi-rong. Particle swarm optimization with adaptive mutation [J]. Acta Electronica Sinica, 2004, 32(3): 416-420(in Chinese)

[10] NoelM M, JannettT C. Simulation of a new hybrid particle swarm optimization algorithm [A]. Proceedings of the Thirty-Sixth Southeastern Symposium on System Theory [C], 2004, Atlanta, USA, IEEE: 150-153

[11] ParsopoulosK E, PlagianakosV P, MagoulasG D, et al.. Stretching technique for obtaining global minimizers through particle swarm optimization [A]. Proceeding of the Workshop on Particle Swarm Optimization [C], 2001, Indianapolis, USA, Russell C. Eberhart: 22-29

[12] BerghF, EngelbrechtA. A new locally convergent particle swarm optimizer [A]. Proceedings of IEEE International Conference on Systems, Man, Cybernetics [C], 2002, Tunisia, IEEE: 96-101

[13] HanifD S, BurakÖ, WarrenP A, et al.. On using exterior penalty approaches for solving linear programming problems [J]. Computer & Operations Research, 2001, 28(11): 1049-1074