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Volume 30 Issue 5
Dec.  2010
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Wei Jing-xuan, Wang Yu-ping. Fuzzy Particle Swarm Optimization for Constrained Optimization Problems[J]. Journal of Electronics & Information Technology, 2008, 30(5): 1218-1221. doi: 10.3724/SP.J.1146.2007.00689
Citation: Wei Jing-xuan, Wang Yu-ping. Fuzzy Particle Swarm Optimization for Constrained Optimization Problems[J]. Journal of Electronics & Information Technology, 2008, 30(5): 1218-1221. doi: 10.3724/SP.J.1146.2007.00689

Fuzzy Particle Swarm Optimization for Constrained Optimization Problems

doi: 10.3724/SP.J.1146.2007.00689
  • Received Date: 2007-05-08
  • Rev Recd Date: 2007-12-25
  • Publish Date: 2008-05-19
  • A fuzzy particle swarm optimization is proposed for solving complex constrained optimization problems. Firstly, a new perturbation operator is designed, and the concepts of fuzzy personal best value and fuzzy global best value are given based on the new operator. Particle updating equations are revised based upon the two new concepts to discourage the premature convergence. Secondly, a new comparison strategy is proposed based on the new concept of infeasible threshold value. It can preserve some infeasible solutions with high quality. Finally, the convergence of this algorithm is proved. The simulation results show that the proposed algorithm is effective, especially for the problems with high dimensions.
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  • Kennedy I and Eberhart R C. Particle swarm optimization[A]. Proc. IEEE Int. Conf. On Neural Networks [C]. Perth,WA, Australia, 1995: 1942-1948.[2]Shi Y and Eberhart R C. A modified swarm optimizer [A].IEEE international conference of Evolutionary Computation[C]. Anchorage, Alaska, 1998: 125-129.[3]Voss M S and Feng Xin. A RMA model selection usingparticle swarm optimization and AIC criteria [A]. 15thTriennial World Congress [C]. Barcelona, Spain: IFAC, 2002:41-45.[4]Bergh F and Engelbrecht A P. Cooperative learning in neuralnetworks using particle swarm optimizers [J]. South Africancomputer Journal, 2000, 11(6): 84-90.[5]Andrews P S. An investigation into mutation operators forparticle swarm optimization [A]. Proceeding of the 2006IEEE Congress on Evolutionary Computation[C], Canada,2006: 1044-1051.[6]李炳宇, 萧蕴诗, 吴启迪. 一种基于微粒群算法求解约束优化问题的混合算法[J]. 控制与决策, 2004, 19(7): 804-807.Li Bing-yu, Xiao Yun-shi, and Wu Qi-di. Hybrid algorithmbased on particle swarm optimization for solving constrainedoptimization problems [J]. Control and Decision, 2004, 19(7):804-807.[7]于繁华, 杨威, 张利彪. 基于模糊的多目标粒子群优化算法及应用[J]. 计算机仿真, 2007, 24(2): 153-156.Yu Fan-hua, Yang-Wei, and Zhang Li-biao. An optimizationarithmetic for multi-objective particle swarm based onfuzziness and its application[J]. Simulation of Computer, 2007,24(2): 153-156.[8]Runarsson T P and Yao X. Stochastic ranking forconstrained evolutionary optimization [J].IEEE Trans. onEvolutionary Computation.2000, 4(3):284-294[9]Venkatraman S and Yen G G. A genetic framework forconstrained optimization using genetic algorithms [J].IEEETrans. on Evolutionary Computation.2005, 9(4):424-435[10]Farmani R and wright J A. Self-adaptive fitness formulationfor constrained optimization [J].IEEE Trans. onEvolutionary Computation.2003, 7(5):445-455[11]Back T. Evolutionary Algorithms in Theory and Practice [M].New York: Oxford University Press, 1996: 21-28.[12]Rudolph G and Agapie A. Convergence properties of somemulti-objective evolutionary algorithms [A]. Proceeding ofthe Congress on Evolutionary Computation[C], Piscataway,2000: 1010-1016.[13]刘淳安, 王宇平. 约束多目标优化问题的进化算法及其收敛性[J]. 系统工程与电子技术, 2007, 29(2): 276-280.Liu Chun-an and Wang Yu-ping. Evolutionary algorithm forconstrained multi-objective optimization problems and itsconvergence [J]. Systems Engineering and Electronics, 2007,29(2): 276-280.
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