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一种解决约束优化问题的模糊粒子群算法

魏静萱 王宇平

魏静萱, 王宇平. 一种解决约束优化问题的模糊粒子群算法[J]. 电子与信息学报, 2008, 30(5): 1218-1221. doi: 10.3724/SP.J.1146.2007.00689
引用本文: 魏静萱, 王宇平. 一种解决约束优化问题的模糊粒子群算法[J]. 电子与信息学报, 2008, 30(5): 1218-1221. doi: 10.3724/SP.J.1146.2007.00689
Wei Jing-xuan, Wang Yu-ping. Fuzzy Particle Swarm Optimization for Constrained Optimization Problems[J]. Journal of Electronics and 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 and Information Technology, 2008, 30(5): 1218-1221. doi: 10.3724/SP.J.1146.2007.00689

一种解决约束优化问题的模糊粒子群算法

doi: 10.3724/SP.J.1146.2007.00689
基金项目: 

国家自然科学基金(60374063)资助课题

Fuzzy Particle Swarm Optimization for Constrained Optimization Problems

  • 摘要: 该文针对复杂约束优化问题,提出了一种模糊粒子群算法(FPSO),设计了一个新的扰动算子,在此基础上定义了模糊个体极值和模糊全局极值,利用这两个定义改进了粒子群进化的方程,利用该方程更新粒子的速度与位置,可以避免早熟收敛问题;定义了不可行度阈值,利用此定义给出了新的粒子比较准则,该准则可以保留一部分性能较优的不可行解微粒。用概率论的有关知识证明了算法的收敛性。仿真结果表明,对于复杂约束优化问题,算法寻优性能优良,特别是对于超高维约束优化问题,该算法获得了更高精度的解。
  • 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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出版历程
  • 收稿日期:  2007-05-08
  • 修回日期:  2007-12-25
  • 刊出日期:  2008-05-19

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