基于随机微粒群算法的改进算法研究
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摘要
微粒群算法(particle swarm optimization,简称PSO)模拟的是鸟群寻找栖息地的行为。微粒群算法简单、易于实现、收敛速度快且需要调整的参数少,自该算法提出以来引起了诸多学者的广泛关注。
论文选取如下两个方面的问题作为研究内容:
(1)在前人工作的基础上将随机微粒群算法和协同进化结合在一起寻求一种更有效的算法。
(2)把微粒群算法和传统优化算法(如信赖域算法、共轭梯度法、最速下降法等)相结合尝试寻找更有效的优化算法。
论文的研究内容主要包括以下几个部分:
第一章是绪论,主要介绍了微粒群算法的历史和现状,本文的创新和突破,以及本文的现实意义。
第二章在多种群协同进化和随机微粒群算法基础上,提出了一种改进的多种群随机微粒群算法,将各个子种群独立地按照随机微粒群进化,周期性的更新共享信息。
第三章在随机微粒群算法和函数梯度信息基础上,提出了基于梯度的随机微粒群算法。数值计算表明算法对于求解连续可微函数的全局优化问题是非常有效的。
第四章提出一种基于协同进化的单纯形随机微粒群算法。该算法采用多个优化种群,分别在奇数种群和偶数种群并行运行随机微粒群法和单纯形法,周期性更新相邻种群最优信息。通过优化两个典型的测试函数验证了算法的有效性。
Particle Swarm Optimization(PSO) is a random optimization algorithm .The basic idea of PSO comes from the research of the behavior of birds find habitat. Since the algorithm has been proposed, because of its easy implementation, fast convergence, and the need to adjust less parameters, attracted wide attention from many scholars. The following two problems have been chosen as the main goal in the dissertation.
(1) Construct a few algorithms based on SPSO and cooperative evolutionary.
(2) Develop a mixed search method by combining SPSO with other optimization algorithms.
The content of this paper is organized into four chapters.
In chapter I, we introduce the history and status about particle swarm optimization, the creativities and the practical importance of this paper.
In chapter II, A modified cooperative stochastic particle swarm optimization(CSPSO), is presented based on the analysis of the SPSO and the cooperative evolutionary PSO with multi-populations. The whole group is divided into several sub-groups. Every subgroup evolved independently and updated sharing information periodically.
In chapter III, The stochastic particle swarm optimization basied the gradient method, is presented based on the analysis of the SPSO and the gradient of the continuous-differential function.Numerical experiments have proved that this hybrid algorithm is very reasonable.
In chapter IV, A cooperative Simplex Method-Stochastic Particle Swarm Optimization(SM-SPSO) is proposed, The conception of multipopulations is adopted in this method,where SPSO and SM run on odd populations and even populations,respectively. Experimental results on optimization two benchmark functions demonstrate its usefulness.
论文选取如下两个方面的问题作为研究内容:
(1)在前人工作的基础上将随机微粒群算法和协同进化结合在一起寻求一种更有效的算法。
(2)把微粒群算法和传统优化算法(如信赖域算法、共轭梯度法、最速下降法等)相结合尝试寻找更有效的优化算法。
论文的研究内容主要包括以下几个部分:
第一章是绪论,主要介绍了微粒群算法的历史和现状,本文的创新和突破,以及本文的现实意义。
第二章在多种群协同进化和随机微粒群算法基础上,提出了一种改进的多种群随机微粒群算法,将各个子种群独立地按照随机微粒群进化,周期性的更新共享信息。
第三章在随机微粒群算法和函数梯度信息基础上,提出了基于梯度的随机微粒群算法。数值计算表明算法对于求解连续可微函数的全局优化问题是非常有效的。
第四章提出一种基于协同进化的单纯形随机微粒群算法。该算法采用多个优化种群,分别在奇数种群和偶数种群并行运行随机微粒群法和单纯形法,周期性更新相邻种群最优信息。通过优化两个典型的测试函数验证了算法的有效性。
Particle Swarm Optimization(PSO) is a random optimization algorithm .The basic idea of PSO comes from the research of the behavior of birds find habitat. Since the algorithm has been proposed, because of its easy implementation, fast convergence, and the need to adjust less parameters, attracted wide attention from many scholars. The following two problems have been chosen as the main goal in the dissertation.
(1) Construct a few algorithms based on SPSO and cooperative evolutionary.
(2) Develop a mixed search method by combining SPSO with other optimization algorithms.
The content of this paper is organized into four chapters.
In chapter I, we introduce the history and status about particle swarm optimization, the creativities and the practical importance of this paper.
In chapter II, A modified cooperative stochastic particle swarm optimization(CSPSO), is presented based on the analysis of the SPSO and the cooperative evolutionary PSO with multi-populations. The whole group is divided into several sub-groups. Every subgroup evolved independently and updated sharing information periodically.
In chapter III, The stochastic particle swarm optimization basied the gradient method, is presented based on the analysis of the SPSO and the gradient of the continuous-differential function.Numerical experiments have proved that this hybrid algorithm is very reasonable.
In chapter IV, A cooperative Simplex Method-Stochastic Particle Swarm Optimization(SM-SPSO) is proposed, The conception of multipopulations is adopted in this method,where SPSO and SM run on odd populations and even populations,respectively. Experimental results on optimization two benchmark functions demonstrate its usefulness.
引文
[1] J Kennedy,R Eberhart. Particle Swarm Optimization[C].In: Proc. of International Conference on Neural Networks, 1995:1942- 1948.
[2] Shi Y, Eberhart R. A modified particle swarm optimizer[C].In: IEEE World Congress on Computational Intelligence,1998:69- 73.
[3] ]Shi Y, Eberhart R C. Fuzzy Adaptive Particle Swarm Optimization [C].In: Proceedings of the IEEE Conference on Evolutionary Computation,Seoul,Korea,201:101～106.
[4] R C Eberhart,Y Shi. Comparing inertia weights and constriction factors in particle swarm optimization [C].In: Proceedings of the IEEE Conference on Evolutionary Computation,ICEC,Vol.1,84～88.
[5] Angeline Peter J. Using selection to improve particle swarm optimization [C].In: Proceedings of the IEEE Conference on Evolutionary Computation,ICEC,1998:84～89.
[6] Lovbjerg M, Rasmussen T k, Krink T. Hybrid Particle Swarm Optimization with Breeding and Subpopulations [C]. In: Proceedings of Genetic and Evolutionary Computation conference,2001.
[7] Natsuki Higashi, Hitoshi Iba. Particle Swarm Optimization with Gaussian Mutation [C].In: Proceedings of the Swarm Intelligence Symposium,SIS 03, IEEE,2003:72- 79.
[8]高鹰,谢胜利.基于模拟退火的粒子群优化算法[J].计算机工程与应用, 2004(1):47- 50.
[9]夏桂梅,曾建潮.基于锦标赛选择遗传算法的随机微粒群算法[J].计算机工程与应用,2007,43(4):51- 53.
[10]Kennedy J. Small Worlds and Mega -Minds: Effects of Neighborhood Topology on Particle Swarm Performance. Proc.of the IEEE Congress of Evolutionary Computation,1999,3:1938.
[11] Suganthan P N. Particle Swarm Optimizer with Neighborhood Operator. Proc. Of the IEEE Congress of Evolutionary Computation,1999,3: 1958.
[12] Kennedy J. Stereotyping: improving particle swarm performance with cluster analysis [A]. Proc. of the 2000 Congress on Evolutionary Computation[C]. Piscataway, NJ, USA: IEEE,2000:1507- 1512.
[13] Hu X, Eberhart R. Multi- objective Optimization Using Dynamic Neighborhood Particle Swarm Optimization [A].In: Proceedings of the IEEE Congress on Evolutionary Computation[C],2002:1677- 1681.
[14] Peer E S, van den Bergh F, Engelbrecht A P. Using neighborhoods with the guaranteed convergence PSO [C]. In: Proceedings of the 2003 IEEE,Swarm Intelligence Symposium, SIS′03,2003:235- 242.
[15]夏小翔,曾建潮,高慧敏.基于元胞自动机的小生境微粒群算法[J].计算机工程与应用,2007,43(11):66- 68.
[16] F van den Bergh, A P Engelberecht. Training Product Unit Networks using Cooperative Particle Swarm Optimizer [C]. In: Proc. of the third Genetic and Evolutionary Computation Conference, San Francisco, USA,2001:126- 131.
[17] Buthainah A. Multi- phase Particle Swarm Optimization [D], Italia: Syracuse University,2002.
[18]薛明志,左秀会,钟伟才,刘静.正交微粒群算法[J].系统仿真学报,2005,17(12):2908- 2911.
[19]滕居特,陈国初,顾幸生.分段式微粒群优化算法[J].华东理工大学学报,2006,32(4):462- 465.
[20]夏桂梅,曾建潮.双群体随机微粒群算法[J].计算机工程与设计,2006,24:46-48
[21]都延丽、吴庆宪、姜长生、周丽协同随机微粒群优化的神经网络预测建模[j]系统工程与电子技术2008,30(8)1540-1544
[22]曾建潮,崔志华.一种保证全局收敛的PSO算法[J].计算机研究与发展,2004,(8):1333- 1338.
[23]陈国初,俞金寿,郭伟.两群替代微粒群优化算法及其应用[J].华东理工大学学报,2005,31(6): 787- 790.
[24]黄建江,须文波,孙俊,董洪伟.量子行为粒子群优化算法的布局问题研究[J].计算机应用,2006,26(12):3015- 3018.
[25]廖京,申群太.差分演化的微粒群算法[J].科学技术与工程,2007,7(8):1628- 1630.
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[27]胡建秀,曾建潮.二阶振荡微粒群算法[J].系统仿真学报,2007,19(5):997- 999.
[28] Kennedy J, Eberhart R C.A Discrete binary version of the Particle Swarm Optimization[C].In: 4Proceedings of the IEEE International Systems, Man and Cybernetics,Vol.5,1997:4104 - 4108.
[29]杨俊杰,周建中,俞菁,吴玮.基于混沌搜索的粒子群优化算法[J].计算机工程与应用,2005,(16):69- 71.
[30] Xiao- Feng Xie,Wen- Jun Zhang,Zhi- Lian Yang.Dissipative particle swarm optimization [C].In: Proceedings of the 2002 Congress on Evolutionary Computation,CEC′02,Vol.2, 2002:1456- 1461.
[31]单世民,邓贵仕.动态环境下一种改进的自适应微粒群算法[J].系统工程理论与实践,2006,(3):39- 44.
[32] Eberhart R,Hu X H. Human tremor analysis using particle swarm optimization [A]. Proceedings of the 1999 Congress on Evolutionary Computation[C].Piscataway,NJ,USA: IEEE,1999:1927- 1930.
[33]李爱国,覃征.积单元神经网络预测噪声环境的混沌时间序列[J].西安科技学院学报,2003,23(3):295- 297.
[34]梁本亮,王增忠,孙富学.基于PSO的结构损伤检测应用研究[J].郑州大学学报,2006,38(4):93- 97.
[35]张坤,吴跃.PSO算法在通信基站分布规划中的应用[J].计算机与数字工程,2006,34(4):135- 138.
[36]韩璞,王学厚,李剑波,王东风.粒子群优化的模糊控制器设计[J].动力工程,2005,25(5):663- 667.
[37]夏蔚军,吴智铭,张伟,杨根科.微粒群优化在Job- shop调度中的应用[J].上海交通大学学报,2005,39(3):381- 385.
[38]肖健梅,李军军,王锡淮.改进微粒群优化算法求解旅行商问题[J].计算机工程与应用,2004,35(16):50- 54.
[39]赵辉,刘鲁源,张更新.基于微粒群优化算法的最优电力系统稳定器设计[J].电网技术,2006,30(3): 32- 35.
[40]田萍芳,王从军.粒子群算法在机械零部件可靠性优化设计的应用[J].自动化仪表,2005,26(7):24- 26.
[41]杜润龙,须文波,孙俊.基于量子微粒群算法的发酵过程模型参数估计[J].计算机工程与设计,2007,28(10):2419- 2421.
[42]陈国初,俞金寿.微粒群神经网络在常压塔汽油干点软测量建模中的应用[J].化工自动化及仪表,2005,32(3): 25- 27.
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[2] Shi Y, Eberhart R. A modified particle swarm optimizer[C].In: IEEE World Congress on Computational Intelligence,1998:69- 73.
[3] ]Shi Y, Eberhart R C. Fuzzy Adaptive Particle Swarm Optimization [C].In: Proceedings of the IEEE Conference on Evolutionary Computation,Seoul,Korea,201:101～106.
[4] R C Eberhart,Y Shi. Comparing inertia weights and constriction factors in particle swarm optimization [C].In: Proceedings of the IEEE Conference on Evolutionary Computation,ICEC,Vol.1,84～88.
[5] Angeline Peter J. Using selection to improve particle swarm optimization [C].In: Proceedings of the IEEE Conference on Evolutionary Computation,ICEC,1998:84～89.
[6] Lovbjerg M, Rasmussen T k, Krink T. Hybrid Particle Swarm Optimization with Breeding and Subpopulations [C]. In: Proceedings of Genetic and Evolutionary Computation conference,2001.
[7] Natsuki Higashi, Hitoshi Iba. Particle Swarm Optimization with Gaussian Mutation [C].In: Proceedings of the Swarm Intelligence Symposium,SIS 03, IEEE,2003:72- 79.
[8]高鹰,谢胜利.基于模拟退火的粒子群优化算法[J].计算机工程与应用, 2004(1):47- 50.
[9]夏桂梅,曾建潮.基于锦标赛选择遗传算法的随机微粒群算法[J].计算机工程与应用,2007,43(4):51- 53.
[10]Kennedy J. Small Worlds and Mega -Minds: Effects of Neighborhood Topology on Particle Swarm Performance. Proc.of the IEEE Congress of Evolutionary Computation,1999,3:1938.
[11] Suganthan P N. Particle Swarm Optimizer with Neighborhood Operator. Proc. Of the IEEE Congress of Evolutionary Computation,1999,3: 1958.
[12] Kennedy J. Stereotyping: improving particle swarm performance with cluster analysis [A]. Proc. of the 2000 Congress on Evolutionary Computation[C]. Piscataway, NJ, USA: IEEE,2000:1507- 1512.
[13] Hu X, Eberhart R. Multi- objective Optimization Using Dynamic Neighborhood Particle Swarm Optimization [A].In: Proceedings of the IEEE Congress on Evolutionary Computation[C],2002:1677- 1681.
[14] Peer E S, van den Bergh F, Engelbrecht A P. Using neighborhoods with the guaranteed convergence PSO [C]. In: Proceedings of the 2003 IEEE,Swarm Intelligence Symposium, SIS′03,2003:235- 242.
[15]夏小翔,曾建潮,高慧敏.基于元胞自动机的小生境微粒群算法[J].计算机工程与应用,2007,43(11):66- 68.
[16] F van den Bergh, A P Engelberecht. Training Product Unit Networks using Cooperative Particle Swarm Optimizer [C]. In: Proc. of the third Genetic and Evolutionary Computation Conference, San Francisco, USA,2001:126- 131.
[17] Buthainah A. Multi- phase Particle Swarm Optimization [D], Italia: Syracuse University,2002.
[18]薛明志,左秀会,钟伟才,刘静.正交微粒群算法[J].系统仿真学报,2005,17(12):2908- 2911.
[19]滕居特,陈国初,顾幸生.分段式微粒群优化算法[J].华东理工大学学报,2006,32(4):462- 465.
[20]夏桂梅,曾建潮.双群体随机微粒群算法[J].计算机工程与设计,2006,24:46-48
[21]都延丽、吴庆宪、姜长生、周丽协同随机微粒群优化的神经网络预测建模[j]系统工程与电子技术2008,30(8)1540-1544
[22]曾建潮,崔志华.一种保证全局收敛的PSO算法[J].计算机研究与发展,2004,(8):1333- 1338.
[23]陈国初,俞金寿,郭伟.两群替代微粒群优化算法及其应用[J].华东理工大学学报,2005,31(6): 787- 790.
[24]黄建江,须文波,孙俊,董洪伟.量子行为粒子群优化算法的布局问题研究[J].计算机应用,2006,26(12):3015- 3018.
[25]廖京,申群太.差分演化的微粒群算法[J].科学技术与工程,2007,7(8):1628- 1630.
[26]姜海明,谢康,王亚非.按概率突跳的改进微粒群优化算法[J].吉林大学学报,2007,37(1):141- 145.
[27]胡建秀,曾建潮.二阶振荡微粒群算法[J].系统仿真学报,2007,19(5):997- 999.
[28] Kennedy J, Eberhart R C.A Discrete binary version of the Particle Swarm Optimization[C].In: 4Proceedings of the IEEE International Systems, Man and Cybernetics,Vol.5,1997:4104 - 4108.
[29]杨俊杰,周建中,俞菁,吴玮.基于混沌搜索的粒子群优化算法[J].计算机工程与应用,2005,(16):69- 71.
[30] Xiao- Feng Xie,Wen- Jun Zhang,Zhi- Lian Yang.Dissipative particle swarm optimization [C].In: Proceedings of the 2002 Congress on Evolutionary Computation,CEC′02,Vol.2, 2002:1456- 1461.
[31]单世民,邓贵仕.动态环境下一种改进的自适应微粒群算法[J].系统工程理论与实践,2006,(3):39- 44.
[32] Eberhart R,Hu X H. Human tremor analysis using particle swarm optimization [A]. Proceedings of the 1999 Congress on Evolutionary Computation[C].Piscataway,NJ,USA: IEEE,1999:1927- 1930.
[33]李爱国,覃征.积单元神经网络预测噪声环境的混沌时间序列[J].西安科技学院学报,2003,23(3):295- 297.
[34]梁本亮,王增忠,孙富学.基于PSO的结构损伤检测应用研究[J].郑州大学学报,2006,38(4):93- 97.
[35]张坤,吴跃.PSO算法在通信基站分布规划中的应用[J].计算机与数字工程,2006,34(4):135- 138.
[36]韩璞,王学厚,李剑波,王东风.粒子群优化的模糊控制器设计[J].动力工程,2005,25(5):663- 667.
[37]夏蔚军,吴智铭,张伟,杨根科.微粒群优化在Job- shop调度中的应用[J].上海交通大学学报,2005,39(3):381- 385.
[38]肖健梅,李军军,王锡淮.改进微粒群优化算法求解旅行商问题[J].计算机工程与应用,2004,35(16):50- 54.
[39]赵辉,刘鲁源,张更新.基于微粒群优化算法的最优电力系统稳定器设计[J].电网技术,2006,30(3): 32- 35.
[40]田萍芳,王从军.粒子群算法在机械零部件可靠性优化设计的应用[J].自动化仪表,2005,26(7):24- 26.
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