人口迁移算法框架描述方法及应用
|
摘要
人口迁移算法是近年来提出的一种新型群智能方法,其理论和算法还不够完善,需要人们不断提出新的理论和方法对其改进。基于此,本文将从人口迁移算法理论框架描述和应用方面进行研究,首先依据群智能优化算法搜索机制的特点构建出人口迁移算法的基本理论框架,对理论框架进行数学化描述,这样可以有助于系统全面的理解算法的原理和本质,有助于今后进一步研究人口迁移算法的相关理论;接着引入混沌优化搜索技术和偏序关系对其进行改进,前一种主要是在算法框架下加入混沌机制改进算法,并通过一算例来验证算法的有效性和正确性,后一种是改进算法的评价方式,用偏序关系取代适应度函数,并举例分析算法的可行性,另外偏序关系为算法的收敛性提供了一定的保证;最后把人口迁移算法应用到径向基函数神经网络的权值训练中,从实验结果分析可看出基于人口迁移算法训练权值方法比RBF算法具有较快的收敛速度和较高的计算精度,验证人口迁移算法的全局优化能力。
Population Migration Algorithm is a new swarm intelligent algorithm proposed recently. Though many numerical experiments illustrated its effectiveness and correctness, its theories and algorithms is still not consummate. People need propose new theories and methods to improve it. As research content, this paper will describe the theoretical framework of population migration algorithm and its application. First this paper describes the PMA based on a given framework of swarm intelligence, it helps to understand the principles of the PMA, as well as combination with chaos theory, a Chaos Population Migration Algorithm is proposed, a numerical experiment example indicates that the new algorithm is effective and accuracy, then with the partial order to replace the fitness function ,and use a example to analysis the algorithm ,else the partial order can make sure of the convergence of the algorithm; Last, this paper try to use algorithm for the weight optimization of RBF neural networks and a model based on this method were presented here, compared with the traditional RBF algorithm, the simulation study indicates that this new method is of high convergence precision and convergence precision.
Population Migration Algorithm is a new swarm intelligent algorithm proposed recently. Though many numerical experiments illustrated its effectiveness and correctness, its theories and algorithms is still not consummate. People need propose new theories and methods to improve it. As research content, this paper will describe the theoretical framework of population migration algorithm and its application. First this paper describes the PMA based on a given framework of swarm intelligence, it helps to understand the principles of the PMA, as well as combination with chaos theory, a Chaos Population Migration Algorithm is proposed, a numerical experiment example indicates that the new algorithm is effective and accuracy, then with the partial order to replace the fitness function ,and use a example to analysis the algorithm ,else the partial order can make sure of the convergence of the algorithm; Last, this paper try to use algorithm for the weight optimization of RBF neural networks and a model based on this method were presented here, compared with the traditional RBF algorithm, the simulation study indicates that this new method is of high convergence precision and convergence precision.
引文
[1]李士勇.模糊控制、神经控制和智能控制论[M].哈尔滨:哈尔滨工业大学出版社,1998.
[2]丁永生.计算智能—理论、技术与应用[M].北京:科学出版社,2004.
[3]高尚,杨静宇.群智能算法及其应用[M].北京:中国水利水电出版社.2006.
[4]彭喜元,彭宇,戴毓丰.群智能理论及应用[J].电子学报,2003,31(12):1982-1987.
[5] Eberhart R.C.,Shi Y.Particle swarm optimization: developments, applications and resources[A].Proc.congress on evolutionary conputation[C].IEEE service center,Piscataway,NJ,Seoul,Korea,27-30 May 2001,1:81-86.
[6]王雪飞.粒子群算法的动态拓扑结构研究[D].重庆:西南大学,2008.
[7] Holland J.H.Adaptation in natural and artificial system[M].Ann Arbor MI: the University of Michigan Press,1975.
[8]杨尚达,李世平.遗言算法研究[J].网络与信息技术,2008,27(9):60-62.
[9] Colorni A.,Dorigo M.,Maniezzo V.Distributed Optimization by Ant Colonies[A].In Proc.of the First European Conf.On Artificial Life[C].Paris,France:Elsevier Publishing,1991.134 - 142.
[10] Dorigo M.,Maniezzo V.,Colorni A.Ant System:Optimization by a Colony of Cooperating Agents[J].IEEE Trans.On Systems,Man,and Cybernetics,1996,26(1):29 - 41.
[11]马良.来自昆虫世界的寻优策略—蚂蚁算法[J].自然杂志,1999,21(3):161-163.
[12] Kennedy J.,Eberhart R.C.Particle Swarm Optimization[A].Proceedings of IEEE International Conference on Neural Networks[C].Perth,Australia,1995,pp.1942-1948.
[13] Kennedy J.,Eberhart R.C.,Shi Y.Swarm Intelligence.San Francisco:Morgan Kaufman Publishers,2001.
[14] Engelbrecht A.P.,Ismail A.Training Product Unit Neural Networks.Stability and Control:Theoryand Applications,1999,2:59-74.
[15] Parsopoulos K.E.,Vrahatis M.N.Particle Swarm Optimization Method in Multiobjective Problems.In:Proc.ACM Symposium on Applied Computing.2002:603-607.
[16] Venayagamoorthy G.K.,Doctor S.Navigation of Mobile Sensors using PSO and Embedded PSO in a Fuzzy Logic Controller.In:IEEE 39th Industry Applications Conference.2004:1200-1206.
[17] Rodriguez A.,Reggia J.A.Extending Self-organizing Particle Systems to Problem Sloving.Artifial Life,2004,10:379-395.
[18] Lovbjerg M.,Rasmussen T.K.,Krink T.Hybrid Particle Swarm Optimiser with Breeding and Subpopulations[A] .In:S pector L,eds.Proc of Genetic and Evolutionary Computation Conference[C].San Fransisco:Mor-gan Kaufmann Publishers Inc,2001:469-476.
[19] Van den Bergh F.,Engelbrecht A.P.A Cooperative Approach to Particle Swarm Optimization.IEEE Trans.on Evout.Comput.,2004,8:225-239.
[20]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002,22(11):76-82.
[21]李晓磊.一种新型的智能优化方法-人工鱼群算法[D].杭州:浙江大学,2003.
[22]李晓磊,钱积新.基于分解协调的人工鱼群优化算法[J].电路与系统学报.2003,8(1):1-6.
[23]李晓磊,路飞,田国会,钱积新.组合优化问题的人工鱼群算法应用[J].山东大学学报(工学版),2004,34(5):64-67.
[24]李晓磊,冯少辉,钱积新等.基于人工鱼群算法的鲁棒PIO控制器参数整定方法研究[J].信息与控制,2004,33(1):112-115.
[25]马建伟,张国立,谢宏等.利用人工鱼群算法优化前向神经网络[J].计算机应用,2004,24(10):21-23.
[26]刘耀年,庞松岭,刘岱.基于人工鱼群算法神经网络的电力系统短期负荷预测[J].电工电能新技术,2005(10):5-8.
[27] Dongdong Wang,Yong quan Zhou,Dunqian Cao.Artificial fish-school algorithm for solving nonlinear equations[A].Joural of Information and Computational ScienceMarch,2007:281-289.
[28]周永华,毛宗源.一种新的全局优化搜索算法—人口迁移算法(I)[J].华南理工大学学报(自然科学版). 2003, 31(3):1- 5.
[29]周永华,毛宗源.一种新的全局优化搜索算法—人口迁移算法(II) [J].华南理工大学学报(自然科学版). 2003, 31(4):41- 43.
[30]武瑛.人口迁移算法的收敛性分析[D].西安:西安科技大学,2005.
[31]陈华锋,谭劲英,李治.人口迁移算法在蛋白质折叠模拟中的应用[J].重庆学院学报(自然科学版)2007,3(2):110-113.
[32]李振涛.改进的人口迁移算法及其应用[D].北京:华北电力大学,2007.
[33]王建勇,陈华锋,莫忠息,牛晓辉,李治.一种改进人口迁移算法在蛋白质折叠模拟中的应用[J].计算机工程与应用,2008,44(2):83-85.
[34]钟建敏,卢雪燕,周永权.改进的人口迁移算法[J].计算机工程与应用,2008,44 (33):218-220.
[35]王晓慧,刘雪英,白梅花.引入高斯变异和最速下降算子的人口迁移算法[J].计算机工程与应用,2009,45(20):57-62.
[36]王丽芳,曾建潮.PSOSA混合优化策略[J].计算机工程与科学,2006,28(9):71-76.
[37]李彬.基于人工鱼群算法的人口迁移算法研究[D].武汉:武汉理工大学,2008.
[38]程乐.新的仿生算法:螳螂算法[J].计算机工程与应用,2008,44(34):44-46.
[39]王凌,刘波.微粒群优化与调度算法[M].北京:清华大学出版社.2008.
[40] De Jong K .A. Evolutionary Computation: A Unified Approach. Cambridge, Mass.: MIT Press,2006.
[41] Moscato P. On evolution, search, optimization, genetic algorithms and martial arts: toward memetic algorithms. Rep.826,Pasadena,CA:California Inst.Technol.,1989.
[42] Ong Y S, Lim M H, Zhu N,et,al. Classification of adaptive memetic algorithms: a comparative study. IEEE Trans.Syst.Man.Cy.B.: Cyberneics,2006,36:141-152.
[43]李振涛,张国立,王淑玲,倪贵博,申红莲.改进人口迁移算法及应用[J].软件时空.2007,23 (12-3):199-200.
[44]王凌,郑大钟,李清生.混沌优化方法的研究进展[J].计算机技术与自动化.2001: 20(1),1-5.
[45]李兵,蒋慰孙.混沌优化方法及其应用[J].控制理论与应用.1997,14(4):613-615.
[46]曾三友,康立山,丁立新.基于偏序关系的演化算法[J].计算机工程.2001,27(8):15-16.
[47]范云棣,盛德成.近世代数.西安:西北工业大学出版社,1980.
[48]郭涛,康力山,李艳.一种求解不等式约束下函数优化问题的新算法[J].武汉大学学报.1999,45(5B):771-775.
[49]韩立群.人工神经网络理论、设计及应用(第二版)[M].北京:化学工业出版社,2007,14-19.
[50] Girosif, Jonesm, Poggio T. Regularization theory and neural networks architectures [J].Neural Computation, 1995, 7(2):219-269.
[51]张良均,曹晶,蒋世忠.神经网络实用教程.北京:机械工业出版社,2007,37-40.
[2]丁永生.计算智能—理论、技术与应用[M].北京:科学出版社,2004.
[3]高尚,杨静宇.群智能算法及其应用[M].北京:中国水利水电出版社.2006.
[4]彭喜元,彭宇,戴毓丰.群智能理论及应用[J].电子学报,2003,31(12):1982-1987.
[5] Eberhart R.C.,Shi Y.Particle swarm optimization: developments, applications and resources[A].Proc.congress on evolutionary conputation[C].IEEE service center,Piscataway,NJ,Seoul,Korea,27-30 May 2001,1:81-86.
[6]王雪飞.粒子群算法的动态拓扑结构研究[D].重庆:西南大学,2008.
[7] Holland J.H.Adaptation in natural and artificial system[M].Ann Arbor MI: the University of Michigan Press,1975.
[8]杨尚达,李世平.遗言算法研究[J].网络与信息技术,2008,27(9):60-62.
[9] Colorni A.,Dorigo M.,Maniezzo V.Distributed Optimization by Ant Colonies[A].In Proc.of the First European Conf.On Artificial Life[C].Paris,France:Elsevier Publishing,1991.134 - 142.
[10] Dorigo M.,Maniezzo V.,Colorni A.Ant System:Optimization by a Colony of Cooperating Agents[J].IEEE Trans.On Systems,Man,and Cybernetics,1996,26(1):29 - 41.
[11]马良.来自昆虫世界的寻优策略—蚂蚁算法[J].自然杂志,1999,21(3):161-163.
[12] Kennedy J.,Eberhart R.C.Particle Swarm Optimization[A].Proceedings of IEEE International Conference on Neural Networks[C].Perth,Australia,1995,pp.1942-1948.
[13] Kennedy J.,Eberhart R.C.,Shi Y.Swarm Intelligence.San Francisco:Morgan Kaufman Publishers,2001.
[14] Engelbrecht A.P.,Ismail A.Training Product Unit Neural Networks.Stability and Control:Theoryand Applications,1999,2:59-74.
[15] Parsopoulos K.E.,Vrahatis M.N.Particle Swarm Optimization Method in Multiobjective Problems.In:Proc.ACM Symposium on Applied Computing.2002:603-607.
[16] Venayagamoorthy G.K.,Doctor S.Navigation of Mobile Sensors using PSO and Embedded PSO in a Fuzzy Logic Controller.In:IEEE 39th Industry Applications Conference.2004:1200-1206.
[17] Rodriguez A.,Reggia J.A.Extending Self-organizing Particle Systems to Problem Sloving.Artifial Life,2004,10:379-395.
[18] Lovbjerg M.,Rasmussen T.K.,Krink T.Hybrid Particle Swarm Optimiser with Breeding and Subpopulations[A] .In:S pector L,eds.Proc of Genetic and Evolutionary Computation Conference[C].San Fransisco:Mor-gan Kaufmann Publishers Inc,2001:469-476.
[19] Van den Bergh F.,Engelbrecht A.P.A Cooperative Approach to Particle Swarm Optimization.IEEE Trans.on Evout.Comput.,2004,8:225-239.
[20]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002,22(11):76-82.
[21]李晓磊.一种新型的智能优化方法-人工鱼群算法[D].杭州:浙江大学,2003.
[22]李晓磊,钱积新.基于分解协调的人工鱼群优化算法[J].电路与系统学报.2003,8(1):1-6.
[23]李晓磊,路飞,田国会,钱积新.组合优化问题的人工鱼群算法应用[J].山东大学学报(工学版),2004,34(5):64-67.
[24]李晓磊,冯少辉,钱积新等.基于人工鱼群算法的鲁棒PIO控制器参数整定方法研究[J].信息与控制,2004,33(1):112-115.
[25]马建伟,张国立,谢宏等.利用人工鱼群算法优化前向神经网络[J].计算机应用,2004,24(10):21-23.
[26]刘耀年,庞松岭,刘岱.基于人工鱼群算法神经网络的电力系统短期负荷预测[J].电工电能新技术,2005(10):5-8.
[27] Dongdong Wang,Yong quan Zhou,Dunqian Cao.Artificial fish-school algorithm for solving nonlinear equations[A].Joural of Information and Computational ScienceMarch,2007:281-289.
[28]周永华,毛宗源.一种新的全局优化搜索算法—人口迁移算法(I)[J].华南理工大学学报(自然科学版). 2003, 31(3):1- 5.
[29]周永华,毛宗源.一种新的全局优化搜索算法—人口迁移算法(II) [J].华南理工大学学报(自然科学版). 2003, 31(4):41- 43.
[30]武瑛.人口迁移算法的收敛性分析[D].西安:西安科技大学,2005.
[31]陈华锋,谭劲英,李治.人口迁移算法在蛋白质折叠模拟中的应用[J].重庆学院学报(自然科学版)2007,3(2):110-113.
[32]李振涛.改进的人口迁移算法及其应用[D].北京:华北电力大学,2007.
[33]王建勇,陈华锋,莫忠息,牛晓辉,李治.一种改进人口迁移算法在蛋白质折叠模拟中的应用[J].计算机工程与应用,2008,44(2):83-85.
[34]钟建敏,卢雪燕,周永权.改进的人口迁移算法[J].计算机工程与应用,2008,44 (33):218-220.
[35]王晓慧,刘雪英,白梅花.引入高斯变异和最速下降算子的人口迁移算法[J].计算机工程与应用,2009,45(20):57-62.
[36]王丽芳,曾建潮.PSOSA混合优化策略[J].计算机工程与科学,2006,28(9):71-76.
[37]李彬.基于人工鱼群算法的人口迁移算法研究[D].武汉:武汉理工大学,2008.
[38]程乐.新的仿生算法:螳螂算法[J].计算机工程与应用,2008,44(34):44-46.
[39]王凌,刘波.微粒群优化与调度算法[M].北京:清华大学出版社.2008.
[40] De Jong K .A. Evolutionary Computation: A Unified Approach. Cambridge, Mass.: MIT Press,2006.
[41] Moscato P. On evolution, search, optimization, genetic algorithms and martial arts: toward memetic algorithms. Rep.826,Pasadena,CA:California Inst.Technol.,1989.
[42] Ong Y S, Lim M H, Zhu N,et,al. Classification of adaptive memetic algorithms: a comparative study. IEEE Trans.Syst.Man.Cy.B.: Cyberneics,2006,36:141-152.
[43]李振涛,张国立,王淑玲,倪贵博,申红莲.改进人口迁移算法及应用[J].软件时空.2007,23 (12-3):199-200.
[44]王凌,郑大钟,李清生.混沌优化方法的研究进展[J].计算机技术与自动化.2001: 20(1),1-5.
[45]李兵,蒋慰孙.混沌优化方法及其应用[J].控制理论与应用.1997,14(4):613-615.
[46]曾三友,康立山,丁立新.基于偏序关系的演化算法[J].计算机工程.2001,27(8):15-16.
[47]范云棣,盛德成.近世代数.西安:西北工业大学出版社,1980.
[48]郭涛,康力山,李艳.一种求解不等式约束下函数优化问题的新算法[J].武汉大学学报.1999,45(5B):771-775.
[49]韩立群.人工神经网络理论、设计及应用(第二版)[M].北京:化学工业出版社,2007,14-19.
[50] Girosif, Jonesm, Poggio T. Regularization theory and neural networks architectures [J].Neural Computation, 1995, 7(2):219-269.
[51]张良均,曹晶,蒋世忠.神经网络实用教程.北京:机械工业出版社,2007,37-40.