搜寻者优化算法及其应用研究
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摘要
优化是科学研究、工程实践和经济管理等诸多领域十分关心的课题,其目的是建立目标函数并找到使目标函数最小或最大的解。随着人类生存空间的扩大以及认识与改造世界的不断深入与发展,各个领域,特别是人工智能与控制领域,存在大量具有多模态、非线性以及建模困难等特点的复杂系统;因此,人们对高效优化技术和智能计算显示出与日俱增的热情和关注,寻求适合大规模并行且具有智能特征的算法已成为许多学科的一个重要研究方向。
社会性动物的个体遵循简单的规则,通过个体与个体之间、个体与环境之间的交流,往往能产生惊人的自组织行为。在过去的20年里,社会性动物的这些行为受到越来越多的研究人员的关注,并受其启发,提出了用于解决分布式优化问题的群体智能算法。优化问题出现在人类生活的方方面面,针对每个问题搜索优化解成了人类的一种基本行为。本文围绕对人类搜索行为的模拟,用于求解优化问题。本文的主要研究成果归纳如下。
(1)提出了云自适应遗传算法(CAGA)。将AGA的作用机理转化为人类特有的自然语言描述:高于种群平均适应度的个体,随着适应度的增加,交叉、变异概率逐渐减小;而低于种群适应度的个体采用最大交叉、变异概率。然后,引入正态云模型,对上述语言描述进行建模,用于自适应确定交叉、变异概率。由于正态云模型的随机性和稳定倾向性特点,使交叉、变异概率既具有传统AGA的趋势性,满足快速寻优能力;又具有随机性,提高了算法避免陷入局部极值的能力。典型函数优化和TSP问题验证了CAGA的有效性。
(2)提出了云进化算法(CEA)。基于连续函数“介值定理”思想和模拟人类的聚焦搜索行为,利用云运算实现交叉、变异操作,提出了CEA。由于正态云模型具有随机性和稳定倾向性的特点,随机性可以保持种群多样性从而避免搜索陷入局部极值,稳定倾向性又可以很好地保护较优个体并对全局最优值进行自适应定位,从而较大程度克服了遗传算法局部搜索能力差、收敛速度慢和进化无记忆性等问题。典型函数优化和FIR数字滤波器设计验证了CEA的有效性。
(3)提出了搜寻者优化算法(SOA)。对人类搜索行为进行深入研究,将优化视为搜寻队伍在搜索空间对最优解的搜索,以搜寻队伍为种群,以搜寻者所处位置为优化问题的候选解,提出了SOA。SOA利用“经验梯度”确定搜索方向、不确定性推理确定步长,完成搜寻者在搜索空间中的位置更新,实现解的优化。
(4)对本文提出的SOA作为一种新的群体智能算法的有效性进行了理论分析,阐述了SOA相对于其它智能优化算法的相同点和不同点,研究了SOA参数对性能的影响;将SOA应用于CEC05的benchmark函数优化,验证了SOA的有效性。
(5)将本文提出的SOA应用于神经网络(ANN)训练。由于ANN性能对网络结构敏感,为了保持网络复杂度和泛化能力问的平衡,同时使用了权值训练、结构进化和优化的正则化性能函数。典型的模式识别和函数逼近问题验证了算法的有效性和优越性。
(6)将本文提出的SOA应用于IIR数字滤波器优化设计。将滤波器的优化设计等价于系统辨识问题,利用SOA调整IIR数字滤波器的系数,使系统的输出与IIR数字滤波器的输出问的均方误差最小,从而将IIR数字滤波器设计的任务转化为一个最小化优化问题。典型实例验证了算法的有效性。
(7)将本文提出的SOA应用于质子交换膜燃料电池(PEMFC)的优化建模。根据PEMFC的极化曲线模型,采用SOA对该模型的参数进行优化,实现了将SOA应用于燃料电池模型优化。仿真结果表明,SOA可有效用于PEMFC建模。
(8)将本文提出的SOA应用于电力系统无功优化。通过调整变压器变比、补偿电容器容量和发电机端电压等控制变量,在尽可能保证各节点电压幅值和发电机无功输出、支路无功功率满足电力系统运行安全和电能质量所需的同时,使有功网损最小。以IEEE 57节点系统为例进行仿真实验,验证了新算法的有效性和优越性。
Optimization has been widely used in many fields such as scientific research, engineering practice and economic management, etc. The aim is to create an objective function and find a solution for minimizing or maximizing it. With the extension of human knowledge and activities, optimization technique and algorithm has become the target of increasing interest due to the increasing demands for improved optimization performance in many complex systems where the involved objective functions are non-linear, multi-modal, and even cannot be expressed in explicit mathematical forms and their derivatives cannot be easily computed. In many research fields, pursuing an effective optimization method has become one of the main objectives for the scientific researchers.
The wonderful and constructive self-organization behavior usually emerges from the individuals of social animals who follow some simple rules and communicate with each other and their environments. In the past 20 years, these behaviors of social animals have been attracting more and more attention of researchers from which swarm intelligence computation was proposed. Swarm intelligence is an algorithm or a device and illumined by the social behavior of gregarious insects and other animals, which is designed for solving distributed problems. Optimization tasks are often encountered in many areas of human life, and the search for a solution to a problem is one of the basic behaviors to all mankind. The dissertation focuses on learning from the advanced social animal, human, and simulating their behaviors for solving optimization problems. The main contributions given in this dissertation are as follows.
(1) A novel cloud-based adaptive genetic algorithm (CAGA) was proposed. The rule for the probability of crossover (mutation) can be described by natural linguistic variables as follows: the probabilities of the individuals with hyper-average fitnesses decrease with the fitness increasing while the probabilities of the individuals with sub-average fitnesses are set at a fixed maximum value. Then, a normal cloud model is introduced to model the rule and adaptively give the probabilities of crossover and mutation. Because cloud model has the properties of randomness and stable tendency, the first property is able to help GA avoid a local optimum, and the second property can improve its convergence speed. The benchmark function optimization and TSP problems proved the effectiveness of CAGA.
(2) A novel cloud-based evolutionary algorithm (CEA) was proposed. Based on the intermediate value theorem of the continuous function and the simulation of human focusing search, CEA is implemented using cloud models as crossover and mutation operators. Unlike GA with the property of "absence of memory", CEA searches the optimal solutions around the current generations until to converge to the optimum for as few generations as possible. Owning to the stable tendency of cloud model, CEA does not easily get lost and is able to locate the region in which the global optimum exists. On the other hand, the randomness of cloud model can maintain the diversity of the population and make CEA enough robust not to get stuck at a local optimum. Benchmark function optimization and digital FIR filter design proved the effectiveness of CEA.
(3) A novel stochastic search algorithm called as seeker optimization algorithm (SOA) was proposed. In SOA, optimization is viewed as a search of search population in the search space. After the detailed study of human searching behaviors, SOA is established with search group as population and the position of a seeker as a candidate solution. In the algorithm, the choice of search direction is based on the empirical gradient by evaluating the response to the position changes, and the decision of step length is based on uncertainty reasoning by using a simple Fuzzy rule. Then, according to the given search direction and step length, the position update is conducted so as to implement the solution evolution.
(4) It is proved that SOA is a new swarm intelligence algorithm. Further theoretical analysis on the validity of SOA was conducted. The difference and sameness between SOA and other intelligent optimization algorithms were presented. The influence of the parameters on the performance was studied. Then, SOA was applied to the benchmark functions of CEC05 and presented the better optimization outputs for some functions. The results of the experiments proved the effectiveness of SOA.
(5) A SOA-based training algorithm was proposed for the evolution of weight values and structure. Since the MLP's performance is sensitive to network architecture, to achieve a proper balance in ANN's network complexity and generalization capability, two approaches are used in this study, namely: structure evolution and the use of the mean squared error with regularization performance function (MSEREG). Hence, the individual in SOA consists of three parts: the link switch bits, the connection weight values and the regularization parameter. The benchmark problems from pattern classification and function approximation were used to evaluate the new algorithm.
(6) A SOA-based evolutionary method is proposed for digital IIR filter design. In this study, IIR filters are designed for the system identification purpose. In this case, the parameters of the IIR filter are successively adjusted by SOA until the error between the outputs of the filter and the system is minimized. Several widely used design examples were used to exhibit the effectiveness of the proposed method.
(7) A new optimized model of proton exchange membrane fuel cell (PEMFC) was proposed by using the proposed SOA. In this case, SOA was used to automatically tune the parameters of the polarization curve model of a PEMFC to minimize the mean square error between the optimized model and the previously sampled experimental data. The simulation results have shown that SOA is suitable for modeling a PEMFC.
(8) The proposed SOA was applied to reactive power optimization. The aim is to minimize the active power loss in the transmission network by tuning the generator voltage, the transformer tap and the shunt capacitor/inductor and simultaneously keeping the load-bus voltage and the generator reactive power within the suitable limits to maintain the voltage quality. The new method is tested on IEEE-57 bus power systems, and the experimental results showed that SOA is suitable for reactive power optimization.
社会性动物的个体遵循简单的规则,通过个体与个体之间、个体与环境之间的交流,往往能产生惊人的自组织行为。在过去的20年里,社会性动物的这些行为受到越来越多的研究人员的关注,并受其启发,提出了用于解决分布式优化问题的群体智能算法。优化问题出现在人类生活的方方面面,针对每个问题搜索优化解成了人类的一种基本行为。本文围绕对人类搜索行为的模拟,用于求解优化问题。本文的主要研究成果归纳如下。
(1)提出了云自适应遗传算法(CAGA)。将AGA的作用机理转化为人类特有的自然语言描述:高于种群平均适应度的个体,随着适应度的增加,交叉、变异概率逐渐减小;而低于种群适应度的个体采用最大交叉、变异概率。然后,引入正态云模型,对上述语言描述进行建模,用于自适应确定交叉、变异概率。由于正态云模型的随机性和稳定倾向性特点,使交叉、变异概率既具有传统AGA的趋势性,满足快速寻优能力;又具有随机性,提高了算法避免陷入局部极值的能力。典型函数优化和TSP问题验证了CAGA的有效性。
(2)提出了云进化算法(CEA)。基于连续函数“介值定理”思想和模拟人类的聚焦搜索行为,利用云运算实现交叉、变异操作,提出了CEA。由于正态云模型具有随机性和稳定倾向性的特点,随机性可以保持种群多样性从而避免搜索陷入局部极值,稳定倾向性又可以很好地保护较优个体并对全局最优值进行自适应定位,从而较大程度克服了遗传算法局部搜索能力差、收敛速度慢和进化无记忆性等问题。典型函数优化和FIR数字滤波器设计验证了CEA的有效性。
(3)提出了搜寻者优化算法(SOA)。对人类搜索行为进行深入研究,将优化视为搜寻队伍在搜索空间对最优解的搜索,以搜寻队伍为种群,以搜寻者所处位置为优化问题的候选解,提出了SOA。SOA利用“经验梯度”确定搜索方向、不确定性推理确定步长,完成搜寻者在搜索空间中的位置更新,实现解的优化。
(4)对本文提出的SOA作为一种新的群体智能算法的有效性进行了理论分析,阐述了SOA相对于其它智能优化算法的相同点和不同点,研究了SOA参数对性能的影响;将SOA应用于CEC05的benchmark函数优化,验证了SOA的有效性。
(5)将本文提出的SOA应用于神经网络(ANN)训练。由于ANN性能对网络结构敏感,为了保持网络复杂度和泛化能力问的平衡,同时使用了权值训练、结构进化和优化的正则化性能函数。典型的模式识别和函数逼近问题验证了算法的有效性和优越性。
(6)将本文提出的SOA应用于IIR数字滤波器优化设计。将滤波器的优化设计等价于系统辨识问题,利用SOA调整IIR数字滤波器的系数,使系统的输出与IIR数字滤波器的输出问的均方误差最小,从而将IIR数字滤波器设计的任务转化为一个最小化优化问题。典型实例验证了算法的有效性。
(7)将本文提出的SOA应用于质子交换膜燃料电池(PEMFC)的优化建模。根据PEMFC的极化曲线模型,采用SOA对该模型的参数进行优化,实现了将SOA应用于燃料电池模型优化。仿真结果表明,SOA可有效用于PEMFC建模。
(8)将本文提出的SOA应用于电力系统无功优化。通过调整变压器变比、补偿电容器容量和发电机端电压等控制变量,在尽可能保证各节点电压幅值和发电机无功输出、支路无功功率满足电力系统运行安全和电能质量所需的同时,使有功网损最小。以IEEE 57节点系统为例进行仿真实验,验证了新算法的有效性和优越性。
Optimization has been widely used in many fields such as scientific research, engineering practice and economic management, etc. The aim is to create an objective function and find a solution for minimizing or maximizing it. With the extension of human knowledge and activities, optimization technique and algorithm has become the target of increasing interest due to the increasing demands for improved optimization performance in many complex systems where the involved objective functions are non-linear, multi-modal, and even cannot be expressed in explicit mathematical forms and their derivatives cannot be easily computed. In many research fields, pursuing an effective optimization method has become one of the main objectives for the scientific researchers.
The wonderful and constructive self-organization behavior usually emerges from the individuals of social animals who follow some simple rules and communicate with each other and their environments. In the past 20 years, these behaviors of social animals have been attracting more and more attention of researchers from which swarm intelligence computation was proposed. Swarm intelligence is an algorithm or a device and illumined by the social behavior of gregarious insects and other animals, which is designed for solving distributed problems. Optimization tasks are often encountered in many areas of human life, and the search for a solution to a problem is one of the basic behaviors to all mankind. The dissertation focuses on learning from the advanced social animal, human, and simulating their behaviors for solving optimization problems. The main contributions given in this dissertation are as follows.
(1) A novel cloud-based adaptive genetic algorithm (CAGA) was proposed. The rule for the probability of crossover (mutation) can be described by natural linguistic variables as follows: the probabilities of the individuals with hyper-average fitnesses decrease with the fitness increasing while the probabilities of the individuals with sub-average fitnesses are set at a fixed maximum value. Then, a normal cloud model is introduced to model the rule and adaptively give the probabilities of crossover and mutation. Because cloud model has the properties of randomness and stable tendency, the first property is able to help GA avoid a local optimum, and the second property can improve its convergence speed. The benchmark function optimization and TSP problems proved the effectiveness of CAGA.
(2) A novel cloud-based evolutionary algorithm (CEA) was proposed. Based on the intermediate value theorem of the continuous function and the simulation of human focusing search, CEA is implemented using cloud models as crossover and mutation operators. Unlike GA with the property of "absence of memory", CEA searches the optimal solutions around the current generations until to converge to the optimum for as few generations as possible. Owning to the stable tendency of cloud model, CEA does not easily get lost and is able to locate the region in which the global optimum exists. On the other hand, the randomness of cloud model can maintain the diversity of the population and make CEA enough robust not to get stuck at a local optimum. Benchmark function optimization and digital FIR filter design proved the effectiveness of CEA.
(3) A novel stochastic search algorithm called as seeker optimization algorithm (SOA) was proposed. In SOA, optimization is viewed as a search of search population in the search space. After the detailed study of human searching behaviors, SOA is established with search group as population and the position of a seeker as a candidate solution. In the algorithm, the choice of search direction is based on the empirical gradient by evaluating the response to the position changes, and the decision of step length is based on uncertainty reasoning by using a simple Fuzzy rule. Then, according to the given search direction and step length, the position update is conducted so as to implement the solution evolution.
(4) It is proved that SOA is a new swarm intelligence algorithm. Further theoretical analysis on the validity of SOA was conducted. The difference and sameness between SOA and other intelligent optimization algorithms were presented. The influence of the parameters on the performance was studied. Then, SOA was applied to the benchmark functions of CEC05 and presented the better optimization outputs for some functions. The results of the experiments proved the effectiveness of SOA.
(5) A SOA-based training algorithm was proposed for the evolution of weight values and structure. Since the MLP's performance is sensitive to network architecture, to achieve a proper balance in ANN's network complexity and generalization capability, two approaches are used in this study, namely: structure evolution and the use of the mean squared error with regularization performance function (MSEREG). Hence, the individual in SOA consists of three parts: the link switch bits, the connection weight values and the regularization parameter. The benchmark problems from pattern classification and function approximation were used to evaluate the new algorithm.
(6) A SOA-based evolutionary method is proposed for digital IIR filter design. In this study, IIR filters are designed for the system identification purpose. In this case, the parameters of the IIR filter are successively adjusted by SOA until the error between the outputs of the filter and the system is minimized. Several widely used design examples were used to exhibit the effectiveness of the proposed method.
(7) A new optimized model of proton exchange membrane fuel cell (PEMFC) was proposed by using the proposed SOA. In this case, SOA was used to automatically tune the parameters of the polarization curve model of a PEMFC to minimize the mean square error between the optimized model and the previously sampled experimental data. The simulation results have shown that SOA is suitable for modeling a PEMFC.
(8) The proposed SOA was applied to reactive power optimization. The aim is to minimize the active power loss in the transmission network by tuning the generator voltage, the transformer tap and the shunt capacitor/inductor and simultaneously keeping the load-bus voltage and the generator reactive power within the suitable limits to maintain the voltage quality. The new method is tested on IEEE-57 bus power systems, and the experimental results showed that SOA is suitable for reactive power optimization.
引文
[1]Adem Kalinli,Nurhan Karaboga."A new method for adaptive IIR filter design based on tabu search algorithm," Int.J.Electron.Commun.(AE(U|¨)),vol.59,no.2,pp.111-117,2005.
[2]陈蕊,夏安邦,马玉龙.电力系统无功优化算法综述.东北电力技术,2006,27(6):38-41.
[3]Adem Kalinli,Nurhan Karaboga."Artificial immune algorithm for IIR filter design,"Journal of Engineering Applications of Artificial Intelligence,vol.18,no.5,pp.919-929,2005.
[4]Ivan Ibarg(U|¨)en Garibay."The proteomics approach to evolutionary computation:An analysis of proteome-based location independent representations based on the proportional genetic algorithm," Ph.D.Thesis,the School of Computer Science in the College of Engineering and Computer Science at the University of Central Florida,Florida,2004.
[5]Marco Dorigo,Mauro Birattari,and Thomas St(u|¨)tzle."Ant colony optimization:artificial ants as a computational intelligence technique," IEEE Computing Intelligence Magazine,vol.1,no.4,pp.28-39,November,2006.
[6]N.Karaboga,A.Kalinli,and D.Karaboga,"Designing IIR filters using ant colony optimization algorithm," Journal of Engineering Applications of Artificial Intelligence,vol.17,no.3,pp.301-309,2004.
[7]Storn R and Price K."Differential Evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces," Technical Report TR95-012,International Computer Science Institute,Berkley,1995.
[8]Kennedy J,Eberhart R."Particle swarm optimization.," In:Proceedings of the 4~(th) IEEE International Conference on Neural Networks,Piscataway:IEEE Service Center,pp.1942-1948,1995.
[9]S Das,A Abraham,A Konar."Particle swarm optimization and differential evolution algorithms:technical analysis,applications and hybridization perspectives," Advances on Conputational Intelligence in Industrial System,Studies in Computational Intelligence,Springer,vol.116,1-38,2008.
[10]Lampinen,J.,Zelinka,I.."On stagnation of the differential evolution algorithm," In:Proceedings of the 6th International Mendel Conference on Soft Computing,Brno,Czech Republic,pp.76-83,2000.
[11]W.B.Langdon and Riccardo Poli."Evolving problems to learn about particle swarm and other optimizers," In:Proceedings of the 2005 IEEE Congress on Evolutionary Computation(CEC 2005),vol.1,pp.81-88,2005
[12]Amin Nobakhti,Hong Wang."A simple self-adaptive differential evolution algorithm with application on the ALSTOM gasifier," Applied Soft Computing,vol.8,no.1,pp.350-370,2008.
[13]Maurice Clerc."Stagnation analysis in particle swarm optimisation or what happens when nothing happens," Online at http://clerc.maurice.free.fr/pso/,2007.
[14]B.Raphael,I.F.C.Smith."A direct stochastic algorithm for global search," Applied Mathematics and Computation,vol.146,no.2-3,pp.729-758,2003.
[15]Wolpert D.H.,Macready W.G.."No free lunch theorems for search," IEEE Trans.Evolutionary Computation,vol.1,no.1,pp.67-82,1997.
[16]Stuart J.Russell,Peter Norvig.Artificial Intelligence:A Modern Approach.Second Edition.Pearson Education Asia Limited and Tsinghua University Press,2006.
[17]L.A.Zadeh."The concept of linguistic variable and its application to approximate reasoning," Information Science,part Ⅰ,no.8,pp.199-246,1975.
[18]李德毅,杜鹢.不确定性人工智能.北京:国防工业出版社,2005.
[19]Robert Michael Lewisa,Virginia Torczon,Michael W.Trosset."Direct search methods:then and now," Journal of Computational and Applied Mathematics,vol.124,no.1-2,pp.191-207,2000.
[20]Patrick Mills,Edward Tsang,Qingfu Zhang,et al."A survey of AI-based meta-heuristics for dealing with local optima in local search," Technical Report Series,Report Number CSM-416,Department of Computer Science,University of Essex,Colchester CO4 3SQ,September 2004.(Available:http://cswww.essex.ac.uk/CSP/)
[21]焦李成,公茂果,邓颖敏等.拉马克进化、班德文效应与自然计算.中国人工智能学会第11届全国学术年会论文集,中国武汉:54-62,2005.
[22]Leandro Nunes de Castro."Fundamentals of natural computing:an overview," Physics of Life Reviews,vol.4,no.1,pp.1-36,2007.
[23]莫宏伟,徐立芳.自然计算与计算主义.中国人工智能学会第11届全国学术年会论文集,中国武汉:210-215,2005.
[24]Andrew J.Greensted and Andy M.Tyrrell."An endocrinologic-inspired hardware implementation of a multicellular system,",in Proceedings of the 2004 NASA/DoD Conference on Evolution Hardware(EH'04),2004.Available at http://www.bioinspired.com/users/ajg112/projects/bionode/.
[25]丁永生.计算智能的新框架:生物网络结构.智能系统学报,2007,2(2):26-30.
[26]龚涛,蔡自兴.自然计算研究进展.控制理论与应用,2006,23(1):79-85.
[27]Kennedy,J.,Eberhart,R.,and Shi,Yu.Swarm Intelligence.Morgan Kaufmann,San Francisco,CA,2001.
[28]A.P.Engelbrecht.Fundamentals of Computational Swarm Intelligence,John Wiley &Sons,USA,2006.
[29]肖人彬,陶振武.群集智能研究进展.管理科学学报,2007,10(3):80-96.
[30]Simon Garnier,-Jacques Gautrais,Guy Theraulaz."The biological principles of swarm intelligence," Swarm Intelligence,vol.1,no.1,pp.3-31,2007.
[31]M.M.Millonas,"Swarms,phase transitions,and collective intelligence," In:Proceedings of Artificial Life Ⅲ,C.G.Langton,ed.,Santa Fe Institute:Addison-Wesley,pp.417-445,1994.
[32]E.Bonabeau,M.Dorigo,G.Theraulaz."Inspiration for optimization from social insect behavior," Nature,vol.406,no.6,pp.39-42,2002.
[33]P.Moscato.Memetic Algorithms' Home Page.[Online].Available:http://www.densis.fee.unicamp.br/～moscato/memetic_home.html.
[34]J.Timmis,T.Knight,L.N.de Castro,et al."An overview of artificial immune systems,"In:R Paton,H Bolouri,M Holcombe,et al,eds.,Proceedings of Computation in Cells and Tissues:Perspectives and Tools for Thought,Natural Computation Series,pp.51-86,Springer,November 2004.
[35]Sun chenyi."Mind-evolution-based machine learning:framework and the implementation of optimization," In:Proceedings of the IEEE Int.Conf on Intelligent Engineering Systems,pp.355-359,1998.
[36]Robert G Reynolds."Using region-schema to solve nonlinear constraint optimization problems:a cultural algorithm approach," In:Festschrift Conference in Honor of John H,Holland,1999.
[37]San Fransisco,CA.C.W.Ahn,R.S.Ramakrishna,D.E.Goldberg."Real-Coded Bayesian Optimization Algorithm:Bringing the Strength of BOA into the Continuous World," In:Proceedings of the Genetic and Evolutionary Computation Conference,Part Ⅰ,pp.840-851,2004.
[38]Anne Auger,Stefan Kern,Nikolaus Hansen."A restart CMA evolution strategy with increasing population size," In:Proceedings of the 2005 IEEE Congress on Evolutionary Computation(CEC 2005),vol.2,019.1769-1776,Edinburgh,Scotland,Sep 02-05,2005.
[39]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.系统工程理论与实践,2002,22(11):32-38.
[40]K.Penev,G.Littlefair,"Free Search-a novel heuristic method," In:Proceedings of the PREP 2003,Exeter,UK,EU,pp.133-134,14-16 April,2003.
[41]Flikkema P.G.,Leid J.G.."Bacterial communities:a microbiological model for swarm intelligence," In:Proceedings of 2005 IEEE Swarm Intelligence Symposium,pp.416-419,2005.
[42]A.Loengarov and V.Tereshko,"A minimal model of honey bee foraging," In:Proceedings of IEEE Swarm Intell.Symp.,pp.175-182,May 2006.
[43]Montiel Oscar,Castillo Oscar,Melin Patricia,et al."Human evolutionary model:a new approach to optimization," Information Sciences,vol.177,no.10,pp.2075-2098,2007.
[44]Xin Yao,Yong Xu."Recent advances in evolutionary computation," J.Comput.Sci.&Technol.,vol.21,no.1,pp.1-18,2006.
[45]D.B.Fogel,Evolutionary Computation." Toward a New Philosophy of Machine Intelligence,3~(rd) ed.,IEEE Press,NY,2006.
[46]Kennet A.De Jong,Evolutionary Computation:A Unified Approach,MIT Press,2006.
[47]Price,K.,Storn,R.,Lampinen,J.,Differential evolution-a practical approach to global optimization.Springer,2005.
[48]J.Brest,S.Greiner,B.Bo(?)kovi(?),et al."Self-adapting control parameters in differential evolution:a comparative study on numerical benchmark problems," IEEE Transactions on Evolutionary Computation,vol.10,no.6,pp.646-657,2006.
[49]A.K.Qin,V.L.Huang and P.N.Suganthan."Differential evolution algorithm with strategy adaptation for global numerical optimization," IEEE Transactions on Evolutionary Computation,in press.
[50]吴亮红.差分进化算法及应用研究.长沙:湖南大学硕士学位论文,2007.
[51]M.M.Ali,A.T(o|¨)rn."Population set-based global optimization algorithms:some modifications and numerical studies," Computers and Operations Research,vol.31,no.10,pp.1703-1725,2004.
[52]P.Kaelo,M.M.Ali."A numerical study of some modified differential evolution algorithms," European Journal of Operational Research,vol.169,no.3,pp.1176-1184,2006.
[53]Junhong Liu,Jouni Lampinen."A fuzzy adaptive differential evolution algorithm," In:Proceedings of IEEE Conf on Computers,Communications,Control and Power Engineering,pp.606-611,2002.
[54]A.K.Qin and P.N.Suganthan."Self-adaptive differential evolution algorithm for numerical optimization," in:Proceedings of IEEE Congress on Evolutionary Computation (CEC 2005),Edinburgh,Scotland,Sep.02-05,2005.
[55]H.Y.Fan and J.Lampinen."A trigonometric mutation operation to differential evolution," Journal of Global Optimization,vol.27,no.1,pp.105-129,2003.
[56]Ji-Pyng Chiou,Feng-Sheng Wang."A hybrid method of differential evolution with application to optimal control problems of a bioprocess system," In:Proceedings of IEEE Evolution Computation Conference,pp.627-632,1998.
[57]Nasimul Noman,Hitoshi Iba."Enhancing differential evolution performance with local search for high dimensional function optimization," In:Proceedings of GECCO'05,pp.967-974,Washington,DC,USA,June 25-29,2005.
[58]Ren(?) Thomsen."Multimodal optimization using crowding-based differential evolution,"In:Proceedings of the IEEE Congress on Evolutionary Computation,vol.2,pp.1382-1389,2004.
[59]Wen-Jun Zhang,Xiao-Feng Xie."DEPSO:Hybrid particle swarm with differential evolution operator," In:Proceedings of the IEEE International Conference on Systems,Man and Cybernetics,vol.4,pp.3816-3821,2003.
[60]M.Varadarajan,K.S.Swarup,"Differential evolutionary algorithm for optimal reactive power dispatch," Int.J.Electr.Power Energ.Syst,2008,in press.
[61]J.Ilonen,J.-K.Kamarainen and J.Lampinen,"Differential evolution training algorithm for feed-forward neural networks," Neural Processing Letters,vol.7,no.1,pp.93-105,2003.
[62]Rainer Storn."Designing nonstandard filters with differential evolution," IEEE Signal Processing Magazine,vol.22,no.1,pp.103-106,2005.
[63]Nurhan Karaboga."Digital IIR filter design using differential evolution algorithm,"EURASIP Journal on Applied Signal Processing,vol.2005,no.1,pp.1269-1276,2005.
[64]J.Kennedy,"The particle swarm:Social adaptation of knowledge," In:Proceedings of IEEE Int.Conf.Evol.Comput.,pp.303-308,Apr.,1997.
[65]Y.del Valle,G.Venayagamoorthy,S.Mohagheghi,et al."Particle Swarm Optimization:Basic Concepts,Variants and Applications in Power Systems," IEEE Transactions on Evolutionary Computation,vol.12,no.2,pp.171-195,2008.
[66]Maurice Clerc.Initialisations for particle swarm optimization.Online at http://clerc.maurice.free.fr/pso/,2008.
[67]J.Kennedy,"Small worlds and mega-minds:Effects of neighborhood topology on particle swarm performance," In:Proceedings of IEEE Congr.Evol.Comput.,vol.3,pp.1931-1938,Jul.1999.
[68]J.Kennedy and R.Mendes,"Population structure and particle swarm performance," In: Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1671-1676,May 2002.
[69]Suganthan,P.N."Particle swarm optimiser with neighbourhood operator," In:Proceedings of the IEEE Congress on Evolutionary Computation,pp.1958-1962,Piscataway,NJ,1999.
[70]K.Veeramachaneni,T.Peram,C.Mohan,et al."Optimization using particle swarms with near neighbor interactions," In:Proceedings of Genetic and Evolutionary Computation (GECCO 2003),vol.2723,pp.110-121,2003.
[71]M.Clerc."Back to random topology," Online at http://clerc.maurice.free.fr/pso/,2007.
[72]Standard PSO 2006/2007(SPSO-06/SPSO-07) on the Particle Swarm Central,Programs section:http://www.particleswarm.info
[73]Mark Richards and Dan Ventura,"Dynamic sociometry in particle swarm optimization",In:Proceedings of International Conference on Computational Intelligence and Natural Computing,pp.1557-1560,September 2003.
[74]L(?)vbjerg M,Rasmussen T K,Krink T."Hybrid particle swarm optimizer with breeding and subpopulations," In:Proceedings of the 3rd Genetic and Evolutionary Computation Conference,San Francisco,USA:Morgan Kaufmann,pp.273-280,2001.
[75]J.Kennedy,"Stereotyping:Improving particle swarm performance with cluster analysis,"In:Proceedings of 2000 Congr.Evolutionary Computing,pp.1507-1512,2000.
[76]X.Li,"Adaptively choosing neighborhood bests using species in a particle swarm optimizer for multimodal function optimization," In:Proceedings of Genetic Evol.Comput.Conf,pp.105-116,Jun.2004.
[77]Stefan Janson and Martin Middendorf."A hierarchical particle swarm optimizer and its adaptive variant," IEEE Transactions on Systems,Man,and Cybernetics-Part B:Cybernetics,vol.35,no.6,pp.1272-1282,2005.
[78]Ben Niu,Yunlong Zhu,Xiaoxian He,et al."A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing," Neurocomputing,vol.71,no.7-9,pp.1436-1448,March 2008.
[79]R.Brits,A.P.Engelbrecht,F.van den Bergh."A niching particle swarm optimizer," In:Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning,Singapore:Orchid Country Club,pp.692-696,2002.
[80]J.J.Liang and P.N.Suganthan."Dynamic multi-swarm particle swarm optimizer with local search," In:Proceedings of the 2005 IEEE Congress on Evolutionary Computation,vol.2,IEEE Press,pp.522-528,2005.
[81]J.Kennedy and R.Mendes,"Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms," In:Proceedings of IEEE Int.Workshop Soft Computing in Industrial Applications,pp.45-50,Jun.2003.
[82]F.Bergh and A.Engelbrecht,"A cooperative approach to particle swarm optimization,"IEEE Trans.Evol.Comput.,vol.8,no.3,pp.225-239,Jun.2004.
[83]S.Baskar and P.N.Suganthan."A novel concurrent particle swarm optimization," In:Proceedings of IEEE Congr.Evol.Comput.,vol.1,pp.792-796,Jun.2004.
[84]M.El-Abd and M.S.Kamel,"A hierarchal cooperative particle swarm optimizer," In:Proceedings of IEEE Swarm Intell.Symp.,pp.43-47,May 2006.
[85]J.J.Liang,A.K.Qin,Ponnuthurai Nagaratnam Suganthan,et al."Comprehensive learning particle swarm optimizer for global optimization of multimodal functions," IEEE Transaction on Evolutionary Computation,vol.10,no.3,pp.67-82,2006.
[86]T.M.Blackwell,P.J.Bentley."Don't push me! Collision-avoiding swarms," In:Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1691-1696,May 2002.
[87]T.M.Blackwell and P.J.Bentley,"Dynamic search with charged swarms," In:Proceedings of Genetic Evol.Comput.Conf,W.B.Langdon et al.,Eds.,pp.19-26,2002.
[88]M.L(?)vbjerg and T.Krink,"Extending particle swarms with self-organized criticality," In:Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1588-1593,May 2002.
[89]Thiemo Krink,Jakob S.Vesterstr(?)m,Jacques Riget."Particle swarm optimization with spatial particle extension," In:Proceedings of the 2002 Congress on Evolutionary Computation,vol.2,pp.1474-1479,2002.
[90]R.Eberhart and Y.Shi,"Particle swarm optimization:Developments,applications and resources," in Proc.IEEE Congr.Evol.Comput.,vol.1,pp.81-86,May 2001.
[91]X.Hu,Y.Shi,and R.Eberhart,"Recent advances in particle swarm," In:Proceedings of IEEE Congr.Evol.Comput.,vol.1,pp.90-97,Jun.2004.
[92]H.Fan and Y.Shi,"Study on Vmax of particle swarm optimization," In:Proceedings of Workshop on Particle Swarm Optimization,Purdue School of Engineering and Technology,Indianapolis,IN,Apr.2001.
[93]A.Abido,"Optimal power flow using particle swarm optimization," Int.J.Elect.Power Energy Syst.,vol.24,no.7,pp.563-571,Oct.2002.
[94]Jacques Riget and Jakob S.Vesterstrom."A diversity-guided particle swarm optimizer-the ARPSO," EVALife Technical Report,no.2002-02,2002.
[95]M.Clerc and J.Kennedy,"The particle swarm-explosion,stability,and convergence in a multidimensional complex space," IEEE Trans.Evol.Comput.,vol.6,no.1,pp.58-73,Feb.2002.
[96]Y.Shi and R.Eberhart,"A modified particle swarm optimizer," In:Proceedings of IEEE World Congr.Comput.Intell.,pp.69-73,May 1998.
[97]X.Chen and Y.Li,"An improved stochastic PSO with high exploration ability," In:Proceedings of IEEE Swarm Intell.Symp.,pp.228-235,May 2006.
[98]Y.Shi and R.Eberhart,"Fuzzy adaptive particle swarm optimization," In:Proceedings of IEEE Congr.Evol.Comput.,vol.1,pp.101-106,May 2001.
[99]S.Doctor,G.Venayagamoorthy,and V.Gudise,"Optimal PSO for collective robotic search applications," In:Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1390-1395,Jun.2004.
[100]M.Khajenejad,F.Afshinmanesh,A.Marandi,and B.N.Araabi,"Intelligent particle swarm optimization using Q-learning," In:Proceedings of IEEE Swarm Intell.Symp.,pp.7-12,May 2006.
[101]W.Zhang,Y.Liu,and M.Clerc,"An adaptive PSO algorithm for reactive power optimization," In:Proceedings of 6th Int.Conf Advances in Power System Control,Operation and Management,pp.302-307,Nov.2003.
[102]Ratnaweera A.,Halgamuge S.K.,Watson H.C."Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients," IEEE Transactions on Evolutionary Computation,vol.8,no.3,pp.240-255,2004
[103]Maurice Clerc.Particle Swarm Optimization.International Scientific and Technical Encyclopedia,2006.
[104]S.Naka,T.Genji,T.Yura,and Y.Fukuyama,"A hybrid particle swarm optimization for distribution state estimation," IEEE Trans.Power Syst.,vol.18,no.1,pp.60-68,Feb.2003.
[105]A.El-Dib,H.Youssef,M.El-Metwally,et al,"Load flow solution using hybrid particle swarm optimization," In:Proceedings of Int.Conf.Elect.,Electron.,Comput.Eng.,pp.742-746,Sep.2004.
[106]Higashi N,Iba H."Particle swarm optimization with Gaussian mutation," In:Proceedings of the 2003 IEEE Swarm Intelligence Symposium,pp.72-79,2003.
[107]V.Miranda and N.Fonseca,"EPSO:evolutionary particle swarm optimization,a new algorithm with applications in power systems," In:Proceedings of IEEE/PES Transmission and Distribution Conf.Exhibition Asia Pacific,vol.2,pp.745-750,Oct.2002.
[108]S.Kannan,S.Slochanal,and N.Padhy,"Application of particle swarm optimization technique and its variants to generation expansion problem," Electric Power Syst.Res.,vol.70,no.3,pp.203-210,Aug.2004.
[109]Kata S,Kalos A,West D."A hybrid swam optimizer for efficient parameter estimation," In:Proceedings of the IEEE Congress on Evolutionary Computation,pp.309-315,2004.
[110]Fan S K,Liang Y C,Zahara E."Hybrid Simplex search and particle swarm optimization for the global optimization of muitimodal functions," Engineering Optimization,vol.36,no.4,pp.401-418,2004.
[111]Victoire T A A,Jeyakumar A E."Hyhrid PSO-SQP for economic dispatch with valve-point effect," Electric Power System Research,vol.71,no.1,pp.51-59,2004.
[112]Xi-Huai,Wang.Jun-Jun,Li,"Hybrid particle swarm optimization with simulated annealing," In:Proceedings of 2004 International Conference on Machine Learning and Cybernetics,ShangHai,pp.2402-2405,2004.
[113]Cui ZH,Zeng JC,Cai XJ."A new stochastic particle swarm optimizer," In:Proceedings of the 2004 Congress on Evolutionary Computation,pp.316-319,2004.
[114]Junying Chen,Zheng Qin,Yu Liu,et al."Particle swarm optimization with local search,"In:Proceedings of Inter.Conf.on Neural Networks and Brain,vol.1,pp.481-484,2005.
[115]X.Xie,W.Zhang,and Z.Yang,"A dissipative particle swarm optimization," In:Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1456-1461,May 2002.
[116]Xiaofeng Xie,Wenjun Zhang,Zhilian Yang."Hybrid particle swarm optimizer with mass extinction," In:Proceedings of 2002 IEEE International Conference on Communications,Circuits and Systems,vol.2,pp.1170-1173,2002.
[117]He S,Wu Q H,Wen JY,et al."A particle swarm optimizer with passive congregation,"Biosystems,vol.78,no.1-3,pp.135-147,2004.
[118]Kang Qi,Wang Lei,and Wu Qidi."A novel self-organizing particle swarm optimization based on gravitation field model," In:Proceedings of the 2007 American Control Conference,New York,USA,pp.528-533,July 11-13,2007.
[119]R.Krohling,"Gaussian swarm:A novel particle swarm optimization algorithm," In:Proceedings of IEEE Con.Cybern.Intell.Syst.,vol.1,pp.372-376,Dec.2004.
[120]K.E.Parsopoulos and M.N.Vrahatis,"On the computation of all global minimizers through particle swarm optimization," IEEE Trans.Evol.Comput.,vol.8,no.3, pp.211-224,Jun.2004.
[121]高鹰,谢胜利.混沌粒子群优化算法.计算机科学,2004,31(8):13-15.
[122]高鹰,谢胜利.免疫粒子群优化算法.计算机工程与应用,2004,40(6):4-6.
[123]S.Yang,M.Wang,and L.Jiao,"A quantum particle swarm optimization," In:Proceedings of IEEE Congr.Evol.Comput.,vol.1,pp.320-324,Jun.2004.
[124]J.Sun,et al,"Particle swarm optimization with particles having quantum behavior," In:Proceedings of 2004 Congress on Evolutionary Computation,pp.325-331,2004.
[125]Monson C K,Sepp i K D."The Kalman swarm:A new approach to particle motion in swarm optimization," In:Proceedings of the Genetic and Evolutionary Computation Conference," pp.140-150,Springer,2004.
[126]Mark S.Voss,"Principal component particle swarm optimization," In:Proceedings of the 2005 IEEE Swarm Intelligence Symposium,Pasadena,California,pp.1-4,June 2005.
[127]Alec Banks,Jonathan Vincent,Chukwudi Anyakoha."A review of particle swarm optimization.Part Ⅰ:background and development," Natural Computing,vol.6,no.4,pp.467-484,2007.Part Ⅱ:hybridization,combinatorial,multicriteria and constrained optimization,and indicative applications," Natural Computing,vol.7,no.1,pp.1-16,2007.
[128]王万良,唐宇.微粒群算法的研究现状与展望.浙江工业大学学报,2007,35(2):136-141.
[129]Riccardo Poli,James Kennedy and Tim Blackwell."Particle swarm optimization:An overview," Swarm Intelligence,vol.1,no.1,pp.33-57,2007.
[130]C.Mohan and B.Al-Kazemi,"Discrete particle swarm optimization," In:Proceedings of Workshop on Particle Swarm Optimization,2001.Purdue School of Engineering and Technology,Indianapolis,IN,Apr.2001.
[131]E.Laskari,K.Parsopoulos,and M.Vrahatis,"Particle swarm optimization for integer programming," In:Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1582-1587,May 2002.
[132]Gregorio Toscano Pulido and Carlos A.Coello Coello."A constraint-handling mechanism for particle swarm optimization," In:Proceedings of the IEEE Congress on Evolutionary Computation,vol.2,pp.1396-1403,2004.
[133]Margarita Reyes-Sierra and Carlos A.Coello Coello."Multi-objective particle swarm optimizers:A Survey of the State-of-the-Art," International Journal of Computational Intelligence Research,vol.2,no.3,pp.287-308,2006.
[134]X.Hu and R.Eberhart,"Adaptive particle swarm optimization:Detection and response to dynamic systems," In:Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1666-1670,May 2002.
[135]D.J.Krusienski and W.K.Jenkins."Design and performance of adaptive systems based on structured stochastic optimization," IEEE Circuits and Systems Magazine,vol.5,no.1,pp.8-20,First Quarter,2005.
[136]V.Mukherjee,S.P.Ghoshal,"Intelligent particle swarm optimized fuzzy PID controller for AVR system," Electr.Power Syst.Res.,vol.77,no.12,pp.1689-1698,2007.
[137]Wachowiak M.P.,Smolikova R.,Zheng Y.F.,et al."An approach to multimodal biomedical image registration utilizing particle swarm optimization," IEEE Transactions on Evolutionary Computation,vol.8,no.3,pp.289-301,2004.
[138]Omran M,Engelbrecht P A and Salman."A particle swarm optimization for image clustering," International Journal of Pattern Recognition and Artificial Intelligence,vol.19,no.3,pp.297-321,2005.
[139]Park J,Choi K,Allstot D J."Parasitic-aware RF circuit design and optimization," IEEE Transactions on Circuits and Systems Ⅰ:Fundamental Theory and Applications,vol.51,no.10,pp.1953-1966,2004.
[140]Rui Mendes,Paulo Cortez,Miguel Rocha,etc.."Particle swarms for feedforward neural network training," In:Proceedings of Inter.Joint Conf Neural Netw.,vol.2,pp.1895-1899,2002.
[141]VG Gudise,GK Venayagamoorthy."Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks," In:Proceedings of IEEE Swarm Intell.Symposium,pp.110-117,2003.
[142]D.E.Goldberg.Genetic Algorithms in Search,Optimization and Machine Learning,Reading,MA:Addison Wesley,1989.
[143]金晶,苏勇.一种改进的自适应遗传算法.计算机工程与应用,2005,41(18):64-69.
[144]Srinivas M,Patnaik L M."Adaptive probabilities of crossover and mutation in genetic algorithm," IEEE Transactions on System,Man and Cybernetics,vol.24,no.4,pp.656-667,1994.
[145]A.E.Eiben,R.Hinterding,Z.Michalewicz."Parameter control in evolutionary algorithms," IEEE Transactions on Evolution computation,vol.3,no.2,pp.124-141,1999.
[146]F.Herrera,M.Lozano."Adaptive genetic operators based on coevolution with fuzzy behaviors," IEEE Trans.on Evolutionary computation,vol.5,no.2,pp.149-165,2001.
[147]Yian Yongqing,Li Zhi,Zhu Zhongying."Building a tax predictive model based on the cloud neural network," Journal of Systems Engineering and Electronics,vol.14,no.3,pp.81-86,2003.
[148]柴日发,徐文骞,曾文华.基于云模型的BP算法改进.计算机仿真,2002,19(3):123-126.
[149]戴卫恒,于全.一种结合知识挖掘的进化规划算法.信号处理,2002,18(3):241-243.
[150]段海滨,王道波,于秀芬等.基于云模型理论的蚁群算法改进研究.哈尔滨工业大学学报,2005,37(1):115-119.
[151]高海昌,冯博琴,朱利.智能优化算法求解TSP问题.控制与决策,2006,21(3):241-247.
[152]蔡之华,彭锦国,高伟等.一种改进的求解TSP问题的演化算法.计算机学报,2005,28(5):823-828.
[153]肖龙光.部分智能优化算法的改进及其数学理论分析.上海:东华大学硕士学位论文,2005:12-15.
[154]陈小平,于盛林.实数遗传算法交叉策略的改进.电子学报,2003,31(1):71-74.
[155]Holland,J.H..Adaptation in Natural and Artificial Systems.The University of Michigen Press,Ann Arbor,MI,1975.
[156]Gexiang Zhang,Yajun Gu,Laizhao Hu,Weidong Jin."A novel genetic algorithm and its application to digital filter design".In:Proceedings of the IEEE 6th International Conference on Intelligent Transportation Systems,shanghai,China,vol.2,pp.1600-1605,2003.
[157]Roberto Irizarry."LARES:An artificial chemical process approach for optimization,"Evolutionary Computation,vol.12,no.4,pp.435-459,2004.
[158]A.Lee,M.Ahmadi,G.A.Jullien,etc.."Design of 1-D FIR filters with genetic algorithms," In:Proceedings of the fifth International Symposium on Signal Processing and its Applications,Brisbane,Australia,1999,Brisbane,Qld.,vol.3,pp.295-298,1999.
[159]D Eustace,DP Barnes,JO Gray."Co-operant mobile robots for industrial applications," In:Proceedings of the International Conference on Industrial Electronics,Control,and Instrumentation,vol.1,pp.39-44,Maui,HI,USA,1993.
[160]Camazine S.,Deneubourg J.-L.,Franks N.,et al.Self-Organization in Biological Systems.Princeton University Press,Princeton,NJ,2001.
[161]Vito Trianni,Roderich Groβ,Thomas H.Labella,et al."Evolving aggregation behaviors in a swarm of robots," W.Banzhaf et al.(Eds.):ECAL 2003,LNAI 2801,pp.865-874,2003.
[162]Wooldridge M.,Jennings N.R.."Intelligent agents:theory and practice," The Knowledge Engineering Review,vol.10,no.2,pp.115-152,1995.
[163]Vignesh Kumar,Ferat Sahin."Cognitive maps in swarm robots for the mine detection application," In:Proceedings of IEEE International Conference on Systems,Man and Cybernetics,vol.4,pp.3364-3369,2003.
[164]Icek Ajzen."Residual effects of past on later behavior:Habituation and reasoned action perspectives," Personality and Social Psychology Review,vol.6,no.2,pp.107-122,2002.
[165]Hans-J(u|¨)rgen Zimmermann."A fresh perspective on uncertainty modeling:uncertainty vs.uncertainty modeling," In:Fuzzy sets and operations research for decision support,Edited by Da Ruan,Chonghu Huang,Beijing:Beijing Normal University Press,pp.40-57,2000.
[166]Steels L."Cooperation between distributed agents through self organization," In:Dernazean Y,M(u|¨)ller J-P,eds.Decentralized AI:Proceedings of the First European Workshop on Modeling Autonomous Agents in a Multi-Agent World(MAAMAW-89).Amsterdam,The Netherlands:Elsevier Science Publishers B.V.pp.175-196,1990.
[167]Lixin Wang."Fuzzy systems are universal approximators," In:Proceedings of IEEE International Conference on Fuzzy Systems,San Diego,CA,USA,pp.1163-1170,1992.
[168]Christopher K.Monson and Kevin D.Seppi."Exposing origin-seeking bias in PSO," In:Proceedings of GECCO'05,pp.241-248,Washington,DC,USA,2005.
[169]Angeline P.J.,"Using Selection to Improve Particle Swarm Optimization," In:Proceedings of the IEEE Congress on Evolutionary Computation,Anchorage,USA,pp.84-89,1998.
[170]P.N.Suganthan,N.Hansen,J.J.Liang,et al."Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization," Technical Report,Nanyang Technological University,Singapore,May 2005 AND KanGAL Report #2005005,IIT Kanpur,India,2005.
[171]Clerc,M."When nearer is better," available from https:/hal.archives-ouvertes.fr/hal-00137320,2007.
[172]Magali R.G.Meireles,Paulo E.M.Almeida,and Marcelo Godoy Sim(?)es."A comprehensive review for industrial applicability of artificial neural networks," IEEE Transactions on Industrial Electronics,vol.50,no.3,pp.585-601,2003.
[173]Miguel Rocha,Paulo Cortez,Jos(?) Neves."Evolution of neural networks for classification and regression," Neurocomputing,vol.70,no.16-18,pp.2809-2816,2007.
[174]X.Yao."Evolving artificial neural networks," Proceedings of the IEEE,vol.87,no.9,pp.1423-1447,1999.
[175]Lutz Prechelt."PROBEN1-A set of benchmarks and benchmarking rules for neural network training algorithms," Technical Report 21/94,Fakult(a|¨)t f(u|¨)r Informatik,Universit(a|¨)t Karlsruhe,Germany.Anonymous FTP:/pub/papers/techreports/1994/1994_21.ps.gz on ftp.ira.uka.de,1994.
[176]Paulito P.Palmes,Taichi Hayasaka,and Shiro Usui."Mutation-based genetic neural network," IEEE Transactions on Neural Networks,vol.16,no.3,pp.587-600,2005.
[177]F.Leung,H.Lam,S.Ling,and P.Tam,"Tuning of the structure and parameters of a neural network using an improved genetic algorithm," IEEE Trans.Neural Netw.,vol.14,no.1,pp.79-88,2003.
[178]Tatsuya Matsunaga,Masahiro Yoshida,and Masaaki Ikehara."Design of IIR digital filters in the complex domain by transforming the desired response," IEEE Trans.on Signal Processing,vol.52,no.7,pp.1975-1982,2004.
[179]Yang Yu and Yu Xinjie."Cooperative coevolutionary genetic algorithm for digital IIR filter design," IEEE Transactions on Industrial Electronics,vol.54,no.3,pp.1311-1318,June 2007.
[180]K.S.Tang,K.F.Man,S.Kwong,and Q.He,"Genetic algorithms and their applications,"IEEE Signal Processing Mag.,vol.13,no.6,pp.22-37,1996.
[181]S.Chen,R.H.Istepanian,and B.L.Luk,"Digital IIR filter design using adaptive simulated annealing," Digital Signal Processing,vol.11,no.3,pp.241-251,2001.
[182]D.J.Krusienski and W.K.Jenkins,"Particle swarm optimization for adaptive IIR filter structures," In:Proceedings of the 2004 Congress on Evolutionary Computation,pp.965-970,June 2004.
[183]James L,Andrew D.Fuel Cell Systems Explained.Chichster:John Wiley & Sons Ltd,2003:14-24.
[184]衣宝廉.燃料电池—原理技术应用.北京:化学出工业出版社,2003:160-236.
[185]M.T.Outeiro,R.Chibante,A.S.Carvalho,et al."A parameter optimized model of a proton exchange membrane fuel cell including temperature effects," Journal of Power Sources (2008),doi:10.1016/j.jpowsour.2008.08.019,in press.
[186]D.M.Guinea,B.Moreno,E.Chinarro,et al."Rotary-gradient fitting algorithm for polarization curves of proton exchange membrane fuel cells(PEMFCs)," International Journal of Hydrogen Energy,vol.33,no.11,pp.2774-2782,2008.
[187]Z.-J.Mo,X.-J.Zhu,L.-Y.Wei,G.-Y.Cao,Parameter optimization for a PEMFC model with a hybrid genetic algorithm," International Journal of Energy Research,vol.30,no.8,pp.585-597,2006.
[188]Zhidan Zhong,Xinjian Zhu,Guangyi Cao,et al."A hybrid multi-variable experimental model for a PEMFC," Journal of Power Sources,vol.164,no.2,pp.746-751,2007.
[189]Samir Jeme(i|¨),Daniel Hissel,Marie-C(?)cile P(?)ra,et al."A new modeling approach of embedded fuel-cell power generators based on artificial neural network," IEEE Transactions on Industrial Electronics,vol.55,no.1,pp.437-447,2008.
[190]李奇,陈维荣,贾俊波等.基于改进粒子群算法的燃料电池模型优化.电池,2007, 37(6):418-421.
[191]Manoj Fozdar,C.M.Arora and V.R.Gottipati."Recent trends in intelligent techniques to power systems," In:Proceedings of the 42nd International Universities Power Engineering Conference,pp.580-591,2007.
[192]B.Zhao,C.X.Guo,Y.J.Cao,"A multi-agent based particle swarm optimization approach for optimal reactive power dispatch," IEEE Tran.Power Systems,vol.20,no.2,pp.1070-1078,2005.
[193]John G.Vlachogiannis and Kwang Y.Lee."A comparative study on particle swarm optimization for optimal steady-state performance of power systems," IEEE Trans.Power Syst.vol.21,no.4,pp.1718-1728,2006.
[194]Wu Q H,Cao Y J,Wen J Y."Optimal reactive power dispatch using an adaptive genetic algorithm," Int.J.Electr Power & Energy Syst.,vol.20,no.8,pp.563-569,1998.
[195]Ray D.Zimmerman,Carlos E.Murillo-S(?)nchez and Deqiang Gan.Matlab Power System Simulation Package(Version 3.1 b2),Available at http://www.pserc.cornell.edu/matpower/.
[196]余娟.无功优化新模型和算法研究及其在电压稳定风险评估中的应用.重庆:重庆大学博士学位论文,2007.
[2]陈蕊,夏安邦,马玉龙.电力系统无功优化算法综述.东北电力技术,2006,27(6):38-41.
[3]Adem Kalinli,Nurhan Karaboga."Artificial immune algorithm for IIR filter design,"Journal of Engineering Applications of Artificial Intelligence,vol.18,no.5,pp.919-929,2005.
[4]Ivan Ibarg(U|¨)en Garibay."The proteomics approach to evolutionary computation:An analysis of proteome-based location independent representations based on the proportional genetic algorithm," Ph.D.Thesis,the School of Computer Science in the College of Engineering and Computer Science at the University of Central Florida,Florida,2004.
[5]Marco Dorigo,Mauro Birattari,and Thomas St(u|¨)tzle."Ant colony optimization:artificial ants as a computational intelligence technique," IEEE Computing Intelligence Magazine,vol.1,no.4,pp.28-39,November,2006.
[6]N.Karaboga,A.Kalinli,and D.Karaboga,"Designing IIR filters using ant colony optimization algorithm," Journal of Engineering Applications of Artificial Intelligence,vol.17,no.3,pp.301-309,2004.
[7]Storn R and Price K."Differential Evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces," Technical Report TR95-012,International Computer Science Institute,Berkley,1995.
[8]Kennedy J,Eberhart R."Particle swarm optimization.," In:Proceedings of the 4~(th) IEEE International Conference on Neural Networks,Piscataway:IEEE Service Center,pp.1942-1948,1995.
[9]S Das,A Abraham,A Konar."Particle swarm optimization and differential evolution algorithms:technical analysis,applications and hybridization perspectives," Advances on Conputational Intelligence in Industrial System,Studies in Computational Intelligence,Springer,vol.116,1-38,2008.
[10]Lampinen,J.,Zelinka,I.."On stagnation of the differential evolution algorithm," In:Proceedings of the 6th International Mendel Conference on Soft Computing,Brno,Czech Republic,pp.76-83,2000.
[11]W.B.Langdon and Riccardo Poli."Evolving problems to learn about particle swarm and other optimizers," In:Proceedings of the 2005 IEEE Congress on Evolutionary Computation(CEC 2005),vol.1,pp.81-88,2005
[12]Amin Nobakhti,Hong Wang."A simple self-adaptive differential evolution algorithm with application on the ALSTOM gasifier," Applied Soft Computing,vol.8,no.1,pp.350-370,2008.
[13]Maurice Clerc."Stagnation analysis in particle swarm optimisation or what happens when nothing happens," Online at http://clerc.maurice.free.fr/pso/,2007.
[14]B.Raphael,I.F.C.Smith."A direct stochastic algorithm for global search," Applied Mathematics and Computation,vol.146,no.2-3,pp.729-758,2003.
[15]Wolpert D.H.,Macready W.G.."No free lunch theorems for search," IEEE Trans.Evolutionary Computation,vol.1,no.1,pp.67-82,1997.
[16]Stuart J.Russell,Peter Norvig.Artificial Intelligence:A Modern Approach.Second Edition.Pearson Education Asia Limited and Tsinghua University Press,2006.
[17]L.A.Zadeh."The concept of linguistic variable and its application to approximate reasoning," Information Science,part Ⅰ,no.8,pp.199-246,1975.
[18]李德毅,杜鹢.不确定性人工智能.北京:国防工业出版社,2005.
[19]Robert Michael Lewisa,Virginia Torczon,Michael W.Trosset."Direct search methods:then and now," Journal of Computational and Applied Mathematics,vol.124,no.1-2,pp.191-207,2000.
[20]Patrick Mills,Edward Tsang,Qingfu Zhang,et al."A survey of AI-based meta-heuristics for dealing with local optima in local search," Technical Report Series,Report Number CSM-416,Department of Computer Science,University of Essex,Colchester CO4 3SQ,September 2004.(Available:http://cswww.essex.ac.uk/CSP/)
[21]焦李成,公茂果,邓颖敏等.拉马克进化、班德文效应与自然计算.中国人工智能学会第11届全国学术年会论文集,中国武汉:54-62,2005.
[22]Leandro Nunes de Castro."Fundamentals of natural computing:an overview," Physics of Life Reviews,vol.4,no.1,pp.1-36,2007.
[23]莫宏伟,徐立芳.自然计算与计算主义.中国人工智能学会第11届全国学术年会论文集,中国武汉:210-215,2005.
[24]Andrew J.Greensted and Andy M.Tyrrell."An endocrinologic-inspired hardware implementation of a multicellular system,",in Proceedings of the 2004 NASA/DoD Conference on Evolution Hardware(EH'04),2004.Available at http://www.bioinspired.com/users/ajg112/projects/bionode/.
[25]丁永生.计算智能的新框架:生物网络结构.智能系统学报,2007,2(2):26-30.
[26]龚涛,蔡自兴.自然计算研究进展.控制理论与应用,2006,23(1):79-85.
[27]Kennedy,J.,Eberhart,R.,and Shi,Yu.Swarm Intelligence.Morgan Kaufmann,San Francisco,CA,2001.
[28]A.P.Engelbrecht.Fundamentals of Computational Swarm Intelligence,John Wiley &Sons,USA,2006.
[29]肖人彬,陶振武.群集智能研究进展.管理科学学报,2007,10(3):80-96.
[30]Simon Garnier,-Jacques Gautrais,Guy Theraulaz."The biological principles of swarm intelligence," Swarm Intelligence,vol.1,no.1,pp.3-31,2007.
[31]M.M.Millonas,"Swarms,phase transitions,and collective intelligence," In:Proceedings of Artificial Life Ⅲ,C.G.Langton,ed.,Santa Fe Institute:Addison-Wesley,pp.417-445,1994.
[32]E.Bonabeau,M.Dorigo,G.Theraulaz."Inspiration for optimization from social insect behavior," Nature,vol.406,no.6,pp.39-42,2002.
[33]P.Moscato.Memetic Algorithms' Home Page.[Online].Available:http://www.densis.fee.unicamp.br/～moscato/memetic_home.html.
[34]J.Timmis,T.Knight,L.N.de Castro,et al."An overview of artificial immune systems,"In:R Paton,H Bolouri,M Holcombe,et al,eds.,Proceedings of Computation in Cells and Tissues:Perspectives and Tools for Thought,Natural Computation Series,pp.51-86,Springer,November 2004.
[35]Sun chenyi."Mind-evolution-based machine learning:framework and the implementation of optimization," In:Proceedings of the IEEE Int.Conf on Intelligent Engineering Systems,pp.355-359,1998.
[36]Robert G Reynolds."Using region-schema to solve nonlinear constraint optimization problems:a cultural algorithm approach," In:Festschrift Conference in Honor of John H,Holland,1999.
[37]San Fransisco,CA.C.W.Ahn,R.S.Ramakrishna,D.E.Goldberg."Real-Coded Bayesian Optimization Algorithm:Bringing the Strength of BOA into the Continuous World," In:Proceedings of the Genetic and Evolutionary Computation Conference,Part Ⅰ,pp.840-851,2004.
[38]Anne Auger,Stefan Kern,Nikolaus Hansen."A restart CMA evolution strategy with increasing population size," In:Proceedings of the 2005 IEEE Congress on Evolutionary Computation(CEC 2005),vol.2,019.1769-1776,Edinburgh,Scotland,Sep 02-05,2005.
[39]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.系统工程理论与实践,2002,22(11):32-38.
[40]K.Penev,G.Littlefair,"Free Search-a novel heuristic method," In:Proceedings of the PREP 2003,Exeter,UK,EU,pp.133-134,14-16 April,2003.
[41]Flikkema P.G.,Leid J.G.."Bacterial communities:a microbiological model for swarm intelligence," In:Proceedings of 2005 IEEE Swarm Intelligence Symposium,pp.416-419,2005.
[42]A.Loengarov and V.Tereshko,"A minimal model of honey bee foraging," In:Proceedings of IEEE Swarm Intell.Symp.,pp.175-182,May 2006.
[43]Montiel Oscar,Castillo Oscar,Melin Patricia,et al."Human evolutionary model:a new approach to optimization," Information Sciences,vol.177,no.10,pp.2075-2098,2007.
[44]Xin Yao,Yong Xu."Recent advances in evolutionary computation," J.Comput.Sci.&Technol.,vol.21,no.1,pp.1-18,2006.
[45]D.B.Fogel,Evolutionary Computation." Toward a New Philosophy of Machine Intelligence,3~(rd) ed.,IEEE Press,NY,2006.
[46]Kennet A.De Jong,Evolutionary Computation:A Unified Approach,MIT Press,2006.
[47]Price,K.,Storn,R.,Lampinen,J.,Differential evolution-a practical approach to global optimization.Springer,2005.
[48]J.Brest,S.Greiner,B.Bo(?)kovi(?),et al."Self-adapting control parameters in differential evolution:a comparative study on numerical benchmark problems," IEEE Transactions on Evolutionary Computation,vol.10,no.6,pp.646-657,2006.
[49]A.K.Qin,V.L.Huang and P.N.Suganthan."Differential evolution algorithm with strategy adaptation for global numerical optimization," IEEE Transactions on Evolutionary Computation,in press.
[50]吴亮红.差分进化算法及应用研究.长沙:湖南大学硕士学位论文,2007.
[51]M.M.Ali,A.T(o|¨)rn."Population set-based global optimization algorithms:some modifications and numerical studies," Computers and Operations Research,vol.31,no.10,pp.1703-1725,2004.
[52]P.Kaelo,M.M.Ali."A numerical study of some modified differential evolution algorithms," European Journal of Operational Research,vol.169,no.3,pp.1176-1184,2006.
[53]Junhong Liu,Jouni Lampinen."A fuzzy adaptive differential evolution algorithm," In:Proceedings of IEEE Conf on Computers,Communications,Control and Power Engineering,pp.606-611,2002.
[54]A.K.Qin and P.N.Suganthan."Self-adaptive differential evolution algorithm for numerical optimization," in:Proceedings of IEEE Congress on Evolutionary Computation (CEC 2005),Edinburgh,Scotland,Sep.02-05,2005.
[55]H.Y.Fan and J.Lampinen."A trigonometric mutation operation to differential evolution," Journal of Global Optimization,vol.27,no.1,pp.105-129,2003.
[56]Ji-Pyng Chiou,Feng-Sheng Wang."A hybrid method of differential evolution with application to optimal control problems of a bioprocess system," In:Proceedings of IEEE Evolution Computation Conference,pp.627-632,1998.
[57]Nasimul Noman,Hitoshi Iba."Enhancing differential evolution performance with local search for high dimensional function optimization," In:Proceedings of GECCO'05,pp.967-974,Washington,DC,USA,June 25-29,2005.
[58]Ren(?) Thomsen."Multimodal optimization using crowding-based differential evolution,"In:Proceedings of the IEEE Congress on Evolutionary Computation,vol.2,pp.1382-1389,2004.
[59]Wen-Jun Zhang,Xiao-Feng Xie."DEPSO:Hybrid particle swarm with differential evolution operator," In:Proceedings of the IEEE International Conference on Systems,Man and Cybernetics,vol.4,pp.3816-3821,2003.
[60]M.Varadarajan,K.S.Swarup,"Differential evolutionary algorithm for optimal reactive power dispatch," Int.J.Electr.Power Energ.Syst,2008,in press.
[61]J.Ilonen,J.-K.Kamarainen and J.Lampinen,"Differential evolution training algorithm for feed-forward neural networks," Neural Processing Letters,vol.7,no.1,pp.93-105,2003.
[62]Rainer Storn."Designing nonstandard filters with differential evolution," IEEE Signal Processing Magazine,vol.22,no.1,pp.103-106,2005.
[63]Nurhan Karaboga."Digital IIR filter design using differential evolution algorithm,"EURASIP Journal on Applied Signal Processing,vol.2005,no.1,pp.1269-1276,2005.
[64]J.Kennedy,"The particle swarm:Social adaptation of knowledge," In:Proceedings of IEEE Int.Conf.Evol.Comput.,pp.303-308,Apr.,1997.
[65]Y.del Valle,G.Venayagamoorthy,S.Mohagheghi,et al."Particle Swarm Optimization:Basic Concepts,Variants and Applications in Power Systems," IEEE Transactions on Evolutionary Computation,vol.12,no.2,pp.171-195,2008.
[66]Maurice Clerc.Initialisations for particle swarm optimization.Online at http://clerc.maurice.free.fr/pso/,2008.
[67]J.Kennedy,"Small worlds and mega-minds:Effects of neighborhood topology on particle swarm performance," In:Proceedings of IEEE Congr.Evol.Comput.,vol.3,pp.1931-1938,Jul.1999.
[68]J.Kennedy and R.Mendes,"Population structure and particle swarm performance," In: Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1671-1676,May 2002.
[69]Suganthan,P.N."Particle swarm optimiser with neighbourhood operator," In:Proceedings of the IEEE Congress on Evolutionary Computation,pp.1958-1962,Piscataway,NJ,1999.
[70]K.Veeramachaneni,T.Peram,C.Mohan,et al."Optimization using particle swarms with near neighbor interactions," In:Proceedings of Genetic and Evolutionary Computation (GECCO 2003),vol.2723,pp.110-121,2003.
[71]M.Clerc."Back to random topology," Online at http://clerc.maurice.free.fr/pso/,2007.
[72]Standard PSO 2006/2007(SPSO-06/SPSO-07) on the Particle Swarm Central,Programs section:http://www.particleswarm.info
[73]Mark Richards and Dan Ventura,"Dynamic sociometry in particle swarm optimization",In:Proceedings of International Conference on Computational Intelligence and Natural Computing,pp.1557-1560,September 2003.
[74]L(?)vbjerg M,Rasmussen T K,Krink T."Hybrid particle swarm optimizer with breeding and subpopulations," In:Proceedings of the 3rd Genetic and Evolutionary Computation Conference,San Francisco,USA:Morgan Kaufmann,pp.273-280,2001.
[75]J.Kennedy,"Stereotyping:Improving particle swarm performance with cluster analysis,"In:Proceedings of 2000 Congr.Evolutionary Computing,pp.1507-1512,2000.
[76]X.Li,"Adaptively choosing neighborhood bests using species in a particle swarm optimizer for multimodal function optimization," In:Proceedings of Genetic Evol.Comput.Conf,pp.105-116,Jun.2004.
[77]Stefan Janson and Martin Middendorf."A hierarchical particle swarm optimizer and its adaptive variant," IEEE Transactions on Systems,Man,and Cybernetics-Part B:Cybernetics,vol.35,no.6,pp.1272-1282,2005.
[78]Ben Niu,Yunlong Zhu,Xiaoxian He,et al."A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing," Neurocomputing,vol.71,no.7-9,pp.1436-1448,March 2008.
[79]R.Brits,A.P.Engelbrecht,F.van den Bergh."A niching particle swarm optimizer," In:Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning,Singapore:Orchid Country Club,pp.692-696,2002.
[80]J.J.Liang and P.N.Suganthan."Dynamic multi-swarm particle swarm optimizer with local search," In:Proceedings of the 2005 IEEE Congress on Evolutionary Computation,vol.2,IEEE Press,pp.522-528,2005.
[81]J.Kennedy and R.Mendes,"Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms," In:Proceedings of IEEE Int.Workshop Soft Computing in Industrial Applications,pp.45-50,Jun.2003.
[82]F.Bergh and A.Engelbrecht,"A cooperative approach to particle swarm optimization,"IEEE Trans.Evol.Comput.,vol.8,no.3,pp.225-239,Jun.2004.
[83]S.Baskar and P.N.Suganthan."A novel concurrent particle swarm optimization," In:Proceedings of IEEE Congr.Evol.Comput.,vol.1,pp.792-796,Jun.2004.
[84]M.El-Abd and M.S.Kamel,"A hierarchal cooperative particle swarm optimizer," In:Proceedings of IEEE Swarm Intell.Symp.,pp.43-47,May 2006.
[85]J.J.Liang,A.K.Qin,Ponnuthurai Nagaratnam Suganthan,et al."Comprehensive learning particle swarm optimizer for global optimization of multimodal functions," IEEE Transaction on Evolutionary Computation,vol.10,no.3,pp.67-82,2006.
[86]T.M.Blackwell,P.J.Bentley."Don't push me! Collision-avoiding swarms," In:Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1691-1696,May 2002.
[87]T.M.Blackwell and P.J.Bentley,"Dynamic search with charged swarms," In:Proceedings of Genetic Evol.Comput.Conf,W.B.Langdon et al.,Eds.,pp.19-26,2002.
[88]M.L(?)vbjerg and T.Krink,"Extending particle swarms with self-organized criticality," In:Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1588-1593,May 2002.
[89]Thiemo Krink,Jakob S.Vesterstr(?)m,Jacques Riget."Particle swarm optimization with spatial particle extension," In:Proceedings of the 2002 Congress on Evolutionary Computation,vol.2,pp.1474-1479,2002.
[90]R.Eberhart and Y.Shi,"Particle swarm optimization:Developments,applications and resources," in Proc.IEEE Congr.Evol.Comput.,vol.1,pp.81-86,May 2001.
[91]X.Hu,Y.Shi,and R.Eberhart,"Recent advances in particle swarm," In:Proceedings of IEEE Congr.Evol.Comput.,vol.1,pp.90-97,Jun.2004.
[92]H.Fan and Y.Shi,"Study on Vmax of particle swarm optimization," In:Proceedings of Workshop on Particle Swarm Optimization,Purdue School of Engineering and Technology,Indianapolis,IN,Apr.2001.
[93]A.Abido,"Optimal power flow using particle swarm optimization," Int.J.Elect.Power Energy Syst.,vol.24,no.7,pp.563-571,Oct.2002.
[94]Jacques Riget and Jakob S.Vesterstrom."A diversity-guided particle swarm optimizer-the ARPSO," EVALife Technical Report,no.2002-02,2002.
[95]M.Clerc and J.Kennedy,"The particle swarm-explosion,stability,and convergence in a multidimensional complex space," IEEE Trans.Evol.Comput.,vol.6,no.1,pp.58-73,Feb.2002.
[96]Y.Shi and R.Eberhart,"A modified particle swarm optimizer," In:Proceedings of IEEE World Congr.Comput.Intell.,pp.69-73,May 1998.
[97]X.Chen and Y.Li,"An improved stochastic PSO with high exploration ability," In:Proceedings of IEEE Swarm Intell.Symp.,pp.228-235,May 2006.
[98]Y.Shi and R.Eberhart,"Fuzzy adaptive particle swarm optimization," In:Proceedings of IEEE Congr.Evol.Comput.,vol.1,pp.101-106,May 2001.
[99]S.Doctor,G.Venayagamoorthy,and V.Gudise,"Optimal PSO for collective robotic search applications," In:Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1390-1395,Jun.2004.
[100]M.Khajenejad,F.Afshinmanesh,A.Marandi,and B.N.Araabi,"Intelligent particle swarm optimization using Q-learning," In:Proceedings of IEEE Swarm Intell.Symp.,pp.7-12,May 2006.
[101]W.Zhang,Y.Liu,and M.Clerc,"An adaptive PSO algorithm for reactive power optimization," In:Proceedings of 6th Int.Conf Advances in Power System Control,Operation and Management,pp.302-307,Nov.2003.
[102]Ratnaweera A.,Halgamuge S.K.,Watson H.C."Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients," IEEE Transactions on Evolutionary Computation,vol.8,no.3,pp.240-255,2004
[103]Maurice Clerc.Particle Swarm Optimization.International Scientific and Technical Encyclopedia,2006.
[104]S.Naka,T.Genji,T.Yura,and Y.Fukuyama,"A hybrid particle swarm optimization for distribution state estimation," IEEE Trans.Power Syst.,vol.18,no.1,pp.60-68,Feb.2003.
[105]A.El-Dib,H.Youssef,M.El-Metwally,et al,"Load flow solution using hybrid particle swarm optimization," In:Proceedings of Int.Conf.Elect.,Electron.,Comput.Eng.,pp.742-746,Sep.2004.
[106]Higashi N,Iba H."Particle swarm optimization with Gaussian mutation," In:Proceedings of the 2003 IEEE Swarm Intelligence Symposium,pp.72-79,2003.
[107]V.Miranda and N.Fonseca,"EPSO:evolutionary particle swarm optimization,a new algorithm with applications in power systems," In:Proceedings of IEEE/PES Transmission and Distribution Conf.Exhibition Asia Pacific,vol.2,pp.745-750,Oct.2002.
[108]S.Kannan,S.Slochanal,and N.Padhy,"Application of particle swarm optimization technique and its variants to generation expansion problem," Electric Power Syst.Res.,vol.70,no.3,pp.203-210,Aug.2004.
[109]Kata S,Kalos A,West D."A hybrid swam optimizer for efficient parameter estimation," In:Proceedings of the IEEE Congress on Evolutionary Computation,pp.309-315,2004.
[110]Fan S K,Liang Y C,Zahara E."Hybrid Simplex search and particle swarm optimization for the global optimization of muitimodal functions," Engineering Optimization,vol.36,no.4,pp.401-418,2004.
[111]Victoire T A A,Jeyakumar A E."Hyhrid PSO-SQP for economic dispatch with valve-point effect," Electric Power System Research,vol.71,no.1,pp.51-59,2004.
[112]Xi-Huai,Wang.Jun-Jun,Li,"Hybrid particle swarm optimization with simulated annealing," In:Proceedings of 2004 International Conference on Machine Learning and Cybernetics,ShangHai,pp.2402-2405,2004.
[113]Cui ZH,Zeng JC,Cai XJ."A new stochastic particle swarm optimizer," In:Proceedings of the 2004 Congress on Evolutionary Computation,pp.316-319,2004.
[114]Junying Chen,Zheng Qin,Yu Liu,et al."Particle swarm optimization with local search,"In:Proceedings of Inter.Conf.on Neural Networks and Brain,vol.1,pp.481-484,2005.
[115]X.Xie,W.Zhang,and Z.Yang,"A dissipative particle swarm optimization," In:Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1456-1461,May 2002.
[116]Xiaofeng Xie,Wenjun Zhang,Zhilian Yang."Hybrid particle swarm optimizer with mass extinction," In:Proceedings of 2002 IEEE International Conference on Communications,Circuits and Systems,vol.2,pp.1170-1173,2002.
[117]He S,Wu Q H,Wen JY,et al."A particle swarm optimizer with passive congregation,"Biosystems,vol.78,no.1-3,pp.135-147,2004.
[118]Kang Qi,Wang Lei,and Wu Qidi."A novel self-organizing particle swarm optimization based on gravitation field model," In:Proceedings of the 2007 American Control Conference,New York,USA,pp.528-533,July 11-13,2007.
[119]R.Krohling,"Gaussian swarm:A novel particle swarm optimization algorithm," In:Proceedings of IEEE Con.Cybern.Intell.Syst.,vol.1,pp.372-376,Dec.2004.
[120]K.E.Parsopoulos and M.N.Vrahatis,"On the computation of all global minimizers through particle swarm optimization," IEEE Trans.Evol.Comput.,vol.8,no.3, pp.211-224,Jun.2004.
[121]高鹰,谢胜利.混沌粒子群优化算法.计算机科学,2004,31(8):13-15.
[122]高鹰,谢胜利.免疫粒子群优化算法.计算机工程与应用,2004,40(6):4-6.
[123]S.Yang,M.Wang,and L.Jiao,"A quantum particle swarm optimization," In:Proceedings of IEEE Congr.Evol.Comput.,vol.1,pp.320-324,Jun.2004.
[124]J.Sun,et al,"Particle swarm optimization with particles having quantum behavior," In:Proceedings of 2004 Congress on Evolutionary Computation,pp.325-331,2004.
[125]Monson C K,Sepp i K D."The Kalman swarm:A new approach to particle motion in swarm optimization," In:Proceedings of the Genetic and Evolutionary Computation Conference," pp.140-150,Springer,2004.
[126]Mark S.Voss,"Principal component particle swarm optimization," In:Proceedings of the 2005 IEEE Swarm Intelligence Symposium,Pasadena,California,pp.1-4,June 2005.
[127]Alec Banks,Jonathan Vincent,Chukwudi Anyakoha."A review of particle swarm optimization.Part Ⅰ:background and development," Natural Computing,vol.6,no.4,pp.467-484,2007.Part Ⅱ:hybridization,combinatorial,multicriteria and constrained optimization,and indicative applications," Natural Computing,vol.7,no.1,pp.1-16,2007.
[128]王万良,唐宇.微粒群算法的研究现状与展望.浙江工业大学学报,2007,35(2):136-141.
[129]Riccardo Poli,James Kennedy and Tim Blackwell."Particle swarm optimization:An overview," Swarm Intelligence,vol.1,no.1,pp.33-57,2007.
[130]C.Mohan and B.Al-Kazemi,"Discrete particle swarm optimization," In:Proceedings of Workshop on Particle Swarm Optimization,2001.Purdue School of Engineering and Technology,Indianapolis,IN,Apr.2001.
[131]E.Laskari,K.Parsopoulos,and M.Vrahatis,"Particle swarm optimization for integer programming," In:Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1582-1587,May 2002.
[132]Gregorio Toscano Pulido and Carlos A.Coello Coello."A constraint-handling mechanism for particle swarm optimization," In:Proceedings of the IEEE Congress on Evolutionary Computation,vol.2,pp.1396-1403,2004.
[133]Margarita Reyes-Sierra and Carlos A.Coello Coello."Multi-objective particle swarm optimizers:A Survey of the State-of-the-Art," International Journal of Computational Intelligence Research,vol.2,no.3,pp.287-308,2006.
[134]X.Hu and R.Eberhart,"Adaptive particle swarm optimization:Detection and response to dynamic systems," In:Proceedings of IEEE Congr.Evol.Comput.,vol.2,pp.1666-1670,May 2002.
[135]D.J.Krusienski and W.K.Jenkins."Design and performance of adaptive systems based on structured stochastic optimization," IEEE Circuits and Systems Magazine,vol.5,no.1,pp.8-20,First Quarter,2005.
[136]V.Mukherjee,S.P.Ghoshal,"Intelligent particle swarm optimized fuzzy PID controller for AVR system," Electr.Power Syst.Res.,vol.77,no.12,pp.1689-1698,2007.
[137]Wachowiak M.P.,Smolikova R.,Zheng Y.F.,et al."An approach to multimodal biomedical image registration utilizing particle swarm optimization," IEEE Transactions on Evolutionary Computation,vol.8,no.3,pp.289-301,2004.
[138]Omran M,Engelbrecht P A and Salman."A particle swarm optimization for image clustering," International Journal of Pattern Recognition and Artificial Intelligence,vol.19,no.3,pp.297-321,2005.
[139]Park J,Choi K,Allstot D J."Parasitic-aware RF circuit design and optimization," IEEE Transactions on Circuits and Systems Ⅰ:Fundamental Theory and Applications,vol.51,no.10,pp.1953-1966,2004.
[140]Rui Mendes,Paulo Cortez,Miguel Rocha,etc.."Particle swarms for feedforward neural network training," In:Proceedings of Inter.Joint Conf Neural Netw.,vol.2,pp.1895-1899,2002.
[141]VG Gudise,GK Venayagamoorthy."Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks," In:Proceedings of IEEE Swarm Intell.Symposium,pp.110-117,2003.
[142]D.E.Goldberg.Genetic Algorithms in Search,Optimization and Machine Learning,Reading,MA:Addison Wesley,1989.
[143]金晶,苏勇.一种改进的自适应遗传算法.计算机工程与应用,2005,41(18):64-69.
[144]Srinivas M,Patnaik L M."Adaptive probabilities of crossover and mutation in genetic algorithm," IEEE Transactions on System,Man and Cybernetics,vol.24,no.4,pp.656-667,1994.
[145]A.E.Eiben,R.Hinterding,Z.Michalewicz."Parameter control in evolutionary algorithms," IEEE Transactions on Evolution computation,vol.3,no.2,pp.124-141,1999.
[146]F.Herrera,M.Lozano."Adaptive genetic operators based on coevolution with fuzzy behaviors," IEEE Trans.on Evolutionary computation,vol.5,no.2,pp.149-165,2001.
[147]Yian Yongqing,Li Zhi,Zhu Zhongying."Building a tax predictive model based on the cloud neural network," Journal of Systems Engineering and Electronics,vol.14,no.3,pp.81-86,2003.
[148]柴日发,徐文骞,曾文华.基于云模型的BP算法改进.计算机仿真,2002,19(3):123-126.
[149]戴卫恒,于全.一种结合知识挖掘的进化规划算法.信号处理,2002,18(3):241-243.
[150]段海滨,王道波,于秀芬等.基于云模型理论的蚁群算法改进研究.哈尔滨工业大学学报,2005,37(1):115-119.
[151]高海昌,冯博琴,朱利.智能优化算法求解TSP问题.控制与决策,2006,21(3):241-247.
[152]蔡之华,彭锦国,高伟等.一种改进的求解TSP问题的演化算法.计算机学报,2005,28(5):823-828.
[153]肖龙光.部分智能优化算法的改进及其数学理论分析.上海:东华大学硕士学位论文,2005:12-15.
[154]陈小平,于盛林.实数遗传算法交叉策略的改进.电子学报,2003,31(1):71-74.
[155]Holland,J.H..Adaptation in Natural and Artificial Systems.The University of Michigen Press,Ann Arbor,MI,1975.
[156]Gexiang Zhang,Yajun Gu,Laizhao Hu,Weidong Jin."A novel genetic algorithm and its application to digital filter design".In:Proceedings of the IEEE 6th International Conference on Intelligent Transportation Systems,shanghai,China,vol.2,pp.1600-1605,2003.
[157]Roberto Irizarry."LARES:An artificial chemical process approach for optimization,"Evolutionary Computation,vol.12,no.4,pp.435-459,2004.
[158]A.Lee,M.Ahmadi,G.A.Jullien,etc.."Design of 1-D FIR filters with genetic algorithms," In:Proceedings of the fifth International Symposium on Signal Processing and its Applications,Brisbane,Australia,1999,Brisbane,Qld.,vol.3,pp.295-298,1999.
[159]D Eustace,DP Barnes,JO Gray."Co-operant mobile robots for industrial applications," In:Proceedings of the International Conference on Industrial Electronics,Control,and Instrumentation,vol.1,pp.39-44,Maui,HI,USA,1993.
[160]Camazine S.,Deneubourg J.-L.,Franks N.,et al.Self-Organization in Biological Systems.Princeton University Press,Princeton,NJ,2001.
[161]Vito Trianni,Roderich Groβ,Thomas H.Labella,et al."Evolving aggregation behaviors in a swarm of robots," W.Banzhaf et al.(Eds.):ECAL 2003,LNAI 2801,pp.865-874,2003.
[162]Wooldridge M.,Jennings N.R.."Intelligent agents:theory and practice," The Knowledge Engineering Review,vol.10,no.2,pp.115-152,1995.
[163]Vignesh Kumar,Ferat Sahin."Cognitive maps in swarm robots for the mine detection application," In:Proceedings of IEEE International Conference on Systems,Man and Cybernetics,vol.4,pp.3364-3369,2003.
[164]Icek Ajzen."Residual effects of past on later behavior:Habituation and reasoned action perspectives," Personality and Social Psychology Review,vol.6,no.2,pp.107-122,2002.
[165]Hans-J(u|¨)rgen Zimmermann."A fresh perspective on uncertainty modeling:uncertainty vs.uncertainty modeling," In:Fuzzy sets and operations research for decision support,Edited by Da Ruan,Chonghu Huang,Beijing:Beijing Normal University Press,pp.40-57,2000.
[166]Steels L."Cooperation between distributed agents through self organization," In:Dernazean Y,M(u|¨)ller J-P,eds.Decentralized AI:Proceedings of the First European Workshop on Modeling Autonomous Agents in a Multi-Agent World(MAAMAW-89).Amsterdam,The Netherlands:Elsevier Science Publishers B.V.pp.175-196,1990.
[167]Lixin Wang."Fuzzy systems are universal approximators," In:Proceedings of IEEE International Conference on Fuzzy Systems,San Diego,CA,USA,pp.1163-1170,1992.
[168]Christopher K.Monson and Kevin D.Seppi."Exposing origin-seeking bias in PSO," In:Proceedings of GECCO'05,pp.241-248,Washington,DC,USA,2005.
[169]Angeline P.J.,"Using Selection to Improve Particle Swarm Optimization," In:Proceedings of the IEEE Congress on Evolutionary Computation,Anchorage,USA,pp.84-89,1998.
[170]P.N.Suganthan,N.Hansen,J.J.Liang,et al."Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization," Technical Report,Nanyang Technological University,Singapore,May 2005 AND KanGAL Report #2005005,IIT Kanpur,India,2005.
[171]Clerc,M."When nearer is better," available from https:/hal.archives-ouvertes.fr/hal-00137320,2007.
[172]Magali R.G.Meireles,Paulo E.M.Almeida,and Marcelo Godoy Sim(?)es."A comprehensive review for industrial applicability of artificial neural networks," IEEE Transactions on Industrial Electronics,vol.50,no.3,pp.585-601,2003.
[173]Miguel Rocha,Paulo Cortez,Jos(?) Neves."Evolution of neural networks for classification and regression," Neurocomputing,vol.70,no.16-18,pp.2809-2816,2007.
[174]X.Yao."Evolving artificial neural networks," Proceedings of the IEEE,vol.87,no.9,pp.1423-1447,1999.
[175]Lutz Prechelt."PROBEN1-A set of benchmarks and benchmarking rules for neural network training algorithms," Technical Report 21/94,Fakult(a|¨)t f(u|¨)r Informatik,Universit(a|¨)t Karlsruhe,Germany.Anonymous FTP:/pub/papers/techreports/1994/1994_21.ps.gz on ftp.ira.uka.de,1994.
[176]Paulito P.Palmes,Taichi Hayasaka,and Shiro Usui."Mutation-based genetic neural network," IEEE Transactions on Neural Networks,vol.16,no.3,pp.587-600,2005.
[177]F.Leung,H.Lam,S.Ling,and P.Tam,"Tuning of the structure and parameters of a neural network using an improved genetic algorithm," IEEE Trans.Neural Netw.,vol.14,no.1,pp.79-88,2003.
[178]Tatsuya Matsunaga,Masahiro Yoshida,and Masaaki Ikehara."Design of IIR digital filters in the complex domain by transforming the desired response," IEEE Trans.on Signal Processing,vol.52,no.7,pp.1975-1982,2004.
[179]Yang Yu and Yu Xinjie."Cooperative coevolutionary genetic algorithm for digital IIR filter design," IEEE Transactions on Industrial Electronics,vol.54,no.3,pp.1311-1318,June 2007.
[180]K.S.Tang,K.F.Man,S.Kwong,and Q.He,"Genetic algorithms and their applications,"IEEE Signal Processing Mag.,vol.13,no.6,pp.22-37,1996.
[181]S.Chen,R.H.Istepanian,and B.L.Luk,"Digital IIR filter design using adaptive simulated annealing," Digital Signal Processing,vol.11,no.3,pp.241-251,2001.
[182]D.J.Krusienski and W.K.Jenkins,"Particle swarm optimization for adaptive IIR filter structures," In:Proceedings of the 2004 Congress on Evolutionary Computation,pp.965-970,June 2004.
[183]James L,Andrew D.Fuel Cell Systems Explained.Chichster:John Wiley & Sons Ltd,2003:14-24.
[184]衣宝廉.燃料电池—原理技术应用.北京:化学出工业出版社,2003:160-236.
[185]M.T.Outeiro,R.Chibante,A.S.Carvalho,et al."A parameter optimized model of a proton exchange membrane fuel cell including temperature effects," Journal of Power Sources (2008),doi:10.1016/j.jpowsour.2008.08.019,in press.
[186]D.M.Guinea,B.Moreno,E.Chinarro,et al."Rotary-gradient fitting algorithm for polarization curves of proton exchange membrane fuel cells(PEMFCs)," International Journal of Hydrogen Energy,vol.33,no.11,pp.2774-2782,2008.
[187]Z.-J.Mo,X.-J.Zhu,L.-Y.Wei,G.-Y.Cao,Parameter optimization for a PEMFC model with a hybrid genetic algorithm," International Journal of Energy Research,vol.30,no.8,pp.585-597,2006.
[188]Zhidan Zhong,Xinjian Zhu,Guangyi Cao,et al."A hybrid multi-variable experimental model for a PEMFC," Journal of Power Sources,vol.164,no.2,pp.746-751,2007.
[189]Samir Jeme(i|¨),Daniel Hissel,Marie-C(?)cile P(?)ra,et al."A new modeling approach of embedded fuel-cell power generators based on artificial neural network," IEEE Transactions on Industrial Electronics,vol.55,no.1,pp.437-447,2008.
[190]李奇,陈维荣,贾俊波等.基于改进粒子群算法的燃料电池模型优化.电池,2007, 37(6):418-421.
[191]Manoj Fozdar,C.M.Arora and V.R.Gottipati."Recent trends in intelligent techniques to power systems," In:Proceedings of the 42nd International Universities Power Engineering Conference,pp.580-591,2007.
[192]B.Zhao,C.X.Guo,Y.J.Cao,"A multi-agent based particle swarm optimization approach for optimal reactive power dispatch," IEEE Tran.Power Systems,vol.20,no.2,pp.1070-1078,2005.
[193]John G.Vlachogiannis and Kwang Y.Lee."A comparative study on particle swarm optimization for optimal steady-state performance of power systems," IEEE Trans.Power Syst.vol.21,no.4,pp.1718-1728,2006.
[194]Wu Q H,Cao Y J,Wen J Y."Optimal reactive power dispatch using an adaptive genetic algorithm," Int.J.Electr Power & Energy Syst.,vol.20,no.8,pp.563-569,1998.
[195]Ray D.Zimmerman,Carlos E.Murillo-S(?)nchez and Deqiang Gan.Matlab Power System Simulation Package(Version 3.1 b2),Available at http://www.pserc.cornell.edu/matpower/.
[196]余娟.无功优化新模型和算法研究及其在电压稳定风险评估中的应用.重庆:重庆大学博士学位论文,2007.