基于混沌云模型的粒子群优化算法
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摘要
针对传统粒子群优化(PSO)算法寻优精度不高和易陷入局部收敛区域的缺点,引入混沌算法和云模型算法对PSO算法的进化机制进行优化,提出混沌云模型粒子群优化(CCMPSO)算法。在算法处于收敛状态时将粒子分为优秀粒子和普通粒子,应用云模型算法和优秀粒子对收敛区域局部求精,发掘全局最优位置;应用混沌算法和普通粒子对收敛区域以外空间进行全局寻优,探索全局最优位置。应用特征根法对CCMPSO算法的收敛性进行分析,并通过仿真实验证明,CCMPSO算法的寻优性能优于其他常用PSO算法。
To deal with the problems of low accuracy and local convergence in conventional Particle Swarm Optimization(PSO) algorithm,the chaos algorithm and cloud model algorithm were introduced into the evolutionary process of PSO algorithm and the chaos cloud model particle swarm optimization(CCMPSO) algorithm was proposed.The particles were divided into excellent particles and normal particles when CCMPSO was in convergent status.To search the global optimum location,the cloud model algorithm as well as excellent particles was applied to local refinement in convergent area,meanwhile chaos algorithm and normal particles were used to global optimization in the outside space of convergent area.The convergence of CCMPSO was analyzed by eigenvalue method.The simulation results prove the CCMPSO has better optimization performance than other main PSO algorithms.
To deal with the problems of low accuracy and local convergence in conventional Particle Swarm Optimization(PSO) algorithm,the chaos algorithm and cloud model algorithm were introduced into the evolutionary process of PSO algorithm and the chaos cloud model particle swarm optimization(CCMPSO) algorithm was proposed.The particles were divided into excellent particles and normal particles when CCMPSO was in convergent status.To search the global optimum location,the cloud model algorithm as well as excellent particles was applied to local refinement in convergent area,meanwhile chaos algorithm and normal particles were used to global optimization in the outside space of convergent area.The convergence of CCMPSO was analyzed by eigenvalue method.The simulation results prove the CCMPSO has better optimization performance than other main PSO algorithms.
引文
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[8]李德毅,孟海军,史学梅.隶属云和隶属云发生器[J].计算机研究与发展,1995,32(6):15-20.
[9]郑春颖,王晓丹,郑全弟,等.自逃逸云简化粒子群优化算法[J].小型微型计算机系统,2010,31(7):1457-1460.
[10]刘衍民,赵庆祯,邵增珍.基于正态云的粒子群优化算法及其应用[J].计算机工程与应用,2011,37(17):161-162,166.
[11]张光卫,何锐,刘禹,等.基于云模型的进化算法[J].计算机学报,2008,31(7):1082-1090.
[12]CLERC M,KENNEDY J.The particle swarm:explosion,stability,and convergence in multi-dimension complex space[J].IEEETransactions on Evolutionary Computation,2002,16(1):58-73.
[2]张朝龙,江巨浪,江善和.基于改进PSO算法的LSSVM入侵检测模型[J].电子技术应用,2010,36(10):132-135.
[3]苏义鑫,沈俊,张丹红,等.神经网络和改进粒子群算法在地震预测中的应用[J].计算机应用,2011,31(7):1793-1796.
[4]SHI Y,EBERHART R C.A modified particle swarm optimizer[C]//IEEE World Congress on Computational Intelligence.Piscat-away:IEEE,1998:69-73.
[5]CLERC M.The swarm and the queen:towards a deterministic andadaptive particle swarm optimization[C]//Proceedings of the 1999Congress on Evolutionary Computation.Piscataway:IEEE,1999:1951-1957.
[6]陈国初,俞金寿.单纯形微粒群优化算法及其应用[J].系统仿真学报,2006,18(4):862-865.
[7]刘军民,高岳林.混沌粒子群优化算法[J].计算机应用,2008,28(2):322-325.
[8]李德毅,孟海军,史学梅.隶属云和隶属云发生器[J].计算机研究与发展,1995,32(6):15-20.
[9]郑春颖,王晓丹,郑全弟,等.自逃逸云简化粒子群优化算法[J].小型微型计算机系统,2010,31(7):1457-1460.
[10]刘衍民,赵庆祯,邵增珍.基于正态云的粒子群优化算法及其应用[J].计算机工程与应用,2011,37(17):161-162,166.
[11]张光卫,何锐,刘禹,等.基于云模型的进化算法[J].计算机学报,2008,31(7):1082-1090.
[12]CLERC M,KENNEDY J.The particle swarm:explosion,stability,and convergence in multi-dimension complex space[J].IEEETransactions on Evolutionary Computation,2002,16(1):58-73.
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