粒子群优化算法研究及其应用
|
摘要
随着不断发展的科学研究和越来越多的应用需求,在工程实践领域中出现了很多复杂的最优化问题。传统的优化方法在求解时往往具有很大的局限性,随着电子计算机技术的发展与使用,采用各种智能优化算法求解复杂优化问题已成为研究热点。
粒子群优化(PSO)算法是一种典型的群智能优化方法。与传统的优化方法相比,PSO算法具有结构简单、参数较少、易于实现以及寻优能力强等优点。然而,PSO算法的理论基础还不够完善,存在早熟收敛、易陷入局部极值等问题,并且在将其应用于工程实际问题时存在很多值得改进和提高之处。通过分析和研究PSO算法的原理,本文提出了PSO算法的控制参数选择策略。为了提高算法的全局搜索能力和收敛速度,本文从不同角度提出了几种改进的PSO算法,并将它们应用于建筑工程项目优化和阵列天线综合。本文的主要研究内容如下：
在PSO算法研究方面,选择合适的控制参数是影响PSO算法性能和效率的关键。在实际应用中,通常只能由设计者根据经验选取最优控制参数,而没有通用的方法。为了找到选择PSO算法控制参数的指导方法,发现影响算法性能的规律,本文采用测试函数对PSO算法中的主要控制参数进行了系统地实验和分析,提出了控制参数取值策略。该策略能明显改进PSO算法性能,具有一定实用价值。
在研究算法理论和信息共享机制的基础上,借鉴人类社会学的分工合作、精英领导等思想,融合混沌优化方法的特点,本文提出了改进的PSO算法。主要包括：
(1)基于模仿人类搜索行为的随机聚焦搜索PSO算法。该算法采用多子群和动态邻域拓扑结构,具有可选参数少、算法简单、计算速度快等优点。实验结果验证了该算法的性能。
(2)基于分层并行协同策略和精英理论的分层多子群PSO算法。各层的子群分别负责不同的搜索任务,粒子的信息是按其性能的级别从高到低逐渐传递,更好地平衡了全局和局部搜索能力。
(3)基于分层多子群的混沌PSO算法。该算法对非线性递减的惯性权重进行混沌变异,全局历史最优位置在更新其每一维分量时,选取不同的个体作为学习对象。其混沌搜索区域半径可自适应地调整。仿真结果表明,该算法能更好地保持种群的多样性,有效避免早熟收敛。
本文将改进的分层多子群PSO算法应用于建筑工程项目综合优化。在工程实例仿真中,该算法能在种群规模较小的情况下,快速找到满意的解。
本文还将改进的PSO算法应用于阵列天线综合。主要包括：
(1)将改进的PSO算法应用于均匀间距直线阵列天线方向图综合中。该算法基于von Neumann邻域结构,采用分层次、多子群策略,以改善其收敛速度和优化精度。顶层和底层的子群分别采用适合其特点的目标函数。仿真结果验证了该算法的有效性。
(2)将混沌搜索和PSO算法相结合,提出了一种改进的PSO算法。该算法采用混沌序列初始化粒子的位置和速度,提高了种群的多样性和粒子搜索的遍历性。当种群的进化出现停滞时,该算法在最优位置的邻域内进行混沌搜索以寻找更好的种群最优位置,其混沌搜索范围可自适应地调整。将该算法应用于均匀间距直线阵列天线方向图综合中,能有效地生成多零陷并抑制旁瓣电平。
(3)将混沌PSO算法应用于均匀间距直线阵、非均匀间距直线阵和共形阵综合中。进化初期,该算法采用综合学习策略更新粒子的速度和位置。当种群陷入停滞时,采用混沌搜索对种群进行扰动。将该算法应用于阵列天线综合中,仿真结果表明,该算法在旁瓣电平抑制、零陷位置生成、零陷深度控制和波瓣赋形等方面优于一些已有文献所报道的结果,具有更好的阵列天线综合能力。
(4)将混沌二进制PSO算法应用于稀布直线阵列和稀布平面阵列天线综合中。为了抑制对称稀布阵列天线的旁瓣电平,该算法对非线性的惯性权重进行混沌变异以提高种群的多样性。与一些已有文献所报道的结果相比,该算法在进化后期具有更好的寻优能力。
With the increasing requirements of scientific research and application, practical optimization problems become more and more complex. Traditional optimization methods often have some limitations when solving these problems. Now, along with the development of computer technology, it has become a new hotspot by using intelligent optimization methods for these complex problems.
As a kind of heuristic optimization algorithm, particle swarm optimization (PSO) algorithm outperforms traditional optimization methods in terms of structural complexity, control parameters, implementation cost and the ability to find the best solution. However, since the theoretical basis of PSO is still far from mature, the problems with the premature convergence remain for exploiting, leading further to more spaces to improve while been applied to practical engineering. Based on studying PSO theory, this work proposes a strategy for choosing the parameters to enhance its dynamic characteristic. Several improved PSO algorithms have been presented to improve the global searching ability and the convergence rate. Then, some modified PSO algorithms are applied to the construction project optimization and antenna array synthesis. The main work can be summarized as follows:
In PSO study, there are a few control parameters affecting its performance. Generally, these parameters are derived from experience. This leads to the difficulty in obtaining the optimal combination of the parameters and affects the use of PSO. In view of this, the effects of the control parameters are systematically investigated by employing benchmark functions. A guideline of choosing these parameters is proposed to improve the performance of PSO.
Based on the study of PSO theory and information sharing mechanism, inspired by some effective ideas of human sociology and combined with the advantages of the chaotic optimization algorithm, several improved PSO algorithms are proposed, which include:
(1) By simulating the act of human randomized searching behaviors, a novel stochastic focusing search PSO (SFSPSO) is proposed. With dynamic neighborhood topology and subpopulation strategy, SFSPSO can improve the global searching ability while keeping the diversity and avoiding the astringency of a local extremum. The simulation results show that SFSPSO is competitive to solve various benchmark problems.
(2) A hierarchical subpopulation PSO (HSPSO) is presented to improve the convergence speed and accuracy by using the strategy of subpopulation hierarchy. The information is gradually flowed from the upper level to the lower level of the hierarchy, and the communication in the particle swarm is moderate and suitable for a proper balance between the global and local searching ability.
(3) A hierarchical chaos PSO (HCPSO) is proposed, which is based on the structure of the hierarchical multi-subpopulations. The novel algorithm adopts chaotic mutation in the nonlinear and decreasing inertia weight. The new global best position is the average position of several individuals that are picked out as exemplars when the new global best positions are updated in each dimension. The radius of the chaotic searching region can be adaptively adjusted. The simulation results show that HCPSO is more effective to overcome the slow convergence and prematurity.
The HSPSO has been applied in time-cost-quality synthesis optimization of the construction project. Satisfied results can be quickly obtained by using HSPSO with a smaller swarm. The exhaustive enumeration is given to verify the effectiveness of HSPSO.
Novel PSO algorithms are also applied to the antenna array synthesis, which include:
(1) The multiple subpopulation PSO (MSPSO) is presented in the pattern synthesis of equally spaced linear arrays. MSPSO is built by employing the strategy of hierarchy and subpopulation with the neighbor structure of von Neumann. A modified objective function model, which utilizes different fitness functions according to the character of the top and bottom layer, is proposed to balance the local and global searching ability. The simulation results show that it achieves relatively high performance and can avoid the premature and easily trapping in local optima.
(2) A chaotic search PSO (CSPSO) is proposed by fusing the advantages of both chaotic optimization algorithm and PSO. The novel algorithm utilizes chaotic searching strategy when the swarm is trapped into stagnancy. To enhance the diversity of samples, several individuals are picked out as exemplars when the new global best position is updated in each dimension. The simulation results show that it achieves relatively high performance by applying CSPSO in the pattern synthesis of antenna arrays with sidelobe reduction and null control.
(3) To deal with the pattern synthesis of the equally spaced linear array, unequally spaced linear array and conformal array, a chaotic PSO (OPSO) is presented. Experimental results present its high performance in the pattern synthesis with low sidelobe level, multi-nulls and shaped beam.
(4) The chaotic binary PSO (CBPSO) is presented as a useful alternative for the synthesis of thinned arrays. CBPSO is improved by nonlinear inertia weight with chaotic mutation to increase the diversity of particles. An extensive numerical analysis has been performed by addressing thinned linear and planar arrays with sidelobe suppression. Simulation results are proposed to compare with published results to verify the effectiveness of the proposed method.
粒子群优化(PSO)算法是一种典型的群智能优化方法。与传统的优化方法相比,PSO算法具有结构简单、参数较少、易于实现以及寻优能力强等优点。然而,PSO算法的理论基础还不够完善,存在早熟收敛、易陷入局部极值等问题,并且在将其应用于工程实际问题时存在很多值得改进和提高之处。通过分析和研究PSO算法的原理,本文提出了PSO算法的控制参数选择策略。为了提高算法的全局搜索能力和收敛速度,本文从不同角度提出了几种改进的PSO算法,并将它们应用于建筑工程项目优化和阵列天线综合。本文的主要研究内容如下：
在PSO算法研究方面,选择合适的控制参数是影响PSO算法性能和效率的关键。在实际应用中,通常只能由设计者根据经验选取最优控制参数,而没有通用的方法。为了找到选择PSO算法控制参数的指导方法,发现影响算法性能的规律,本文采用测试函数对PSO算法中的主要控制参数进行了系统地实验和分析,提出了控制参数取值策略。该策略能明显改进PSO算法性能,具有一定实用价值。
在研究算法理论和信息共享机制的基础上,借鉴人类社会学的分工合作、精英领导等思想,融合混沌优化方法的特点,本文提出了改进的PSO算法。主要包括：
(1)基于模仿人类搜索行为的随机聚焦搜索PSO算法。该算法采用多子群和动态邻域拓扑结构,具有可选参数少、算法简单、计算速度快等优点。实验结果验证了该算法的性能。
(2)基于分层并行协同策略和精英理论的分层多子群PSO算法。各层的子群分别负责不同的搜索任务,粒子的信息是按其性能的级别从高到低逐渐传递,更好地平衡了全局和局部搜索能力。
(3)基于分层多子群的混沌PSO算法。该算法对非线性递减的惯性权重进行混沌变异,全局历史最优位置在更新其每一维分量时,选取不同的个体作为学习对象。其混沌搜索区域半径可自适应地调整。仿真结果表明,该算法能更好地保持种群的多样性,有效避免早熟收敛。
本文将改进的分层多子群PSO算法应用于建筑工程项目综合优化。在工程实例仿真中,该算法能在种群规模较小的情况下,快速找到满意的解。
本文还将改进的PSO算法应用于阵列天线综合。主要包括：
(1)将改进的PSO算法应用于均匀间距直线阵列天线方向图综合中。该算法基于von Neumann邻域结构,采用分层次、多子群策略,以改善其收敛速度和优化精度。顶层和底层的子群分别采用适合其特点的目标函数。仿真结果验证了该算法的有效性。
(2)将混沌搜索和PSO算法相结合,提出了一种改进的PSO算法。该算法采用混沌序列初始化粒子的位置和速度,提高了种群的多样性和粒子搜索的遍历性。当种群的进化出现停滞时,该算法在最优位置的邻域内进行混沌搜索以寻找更好的种群最优位置,其混沌搜索范围可自适应地调整。将该算法应用于均匀间距直线阵列天线方向图综合中,能有效地生成多零陷并抑制旁瓣电平。
(3)将混沌PSO算法应用于均匀间距直线阵、非均匀间距直线阵和共形阵综合中。进化初期,该算法采用综合学习策略更新粒子的速度和位置。当种群陷入停滞时,采用混沌搜索对种群进行扰动。将该算法应用于阵列天线综合中,仿真结果表明,该算法在旁瓣电平抑制、零陷位置生成、零陷深度控制和波瓣赋形等方面优于一些已有文献所报道的结果,具有更好的阵列天线综合能力。
(4)将混沌二进制PSO算法应用于稀布直线阵列和稀布平面阵列天线综合中。为了抑制对称稀布阵列天线的旁瓣电平,该算法对非线性的惯性权重进行混沌变异以提高种群的多样性。与一些已有文献所报道的结果相比,该算法在进化后期具有更好的寻优能力。
With the increasing requirements of scientific research and application, practical optimization problems become more and more complex. Traditional optimization methods often have some limitations when solving these problems. Now, along with the development of computer technology, it has become a new hotspot by using intelligent optimization methods for these complex problems.
As a kind of heuristic optimization algorithm, particle swarm optimization (PSO) algorithm outperforms traditional optimization methods in terms of structural complexity, control parameters, implementation cost and the ability to find the best solution. However, since the theoretical basis of PSO is still far from mature, the problems with the premature convergence remain for exploiting, leading further to more spaces to improve while been applied to practical engineering. Based on studying PSO theory, this work proposes a strategy for choosing the parameters to enhance its dynamic characteristic. Several improved PSO algorithms have been presented to improve the global searching ability and the convergence rate. Then, some modified PSO algorithms are applied to the construction project optimization and antenna array synthesis. The main work can be summarized as follows:
In PSO study, there are a few control parameters affecting its performance. Generally, these parameters are derived from experience. This leads to the difficulty in obtaining the optimal combination of the parameters and affects the use of PSO. In view of this, the effects of the control parameters are systematically investigated by employing benchmark functions. A guideline of choosing these parameters is proposed to improve the performance of PSO.
Based on the study of PSO theory and information sharing mechanism, inspired by some effective ideas of human sociology and combined with the advantages of the chaotic optimization algorithm, several improved PSO algorithms are proposed, which include:
(1) By simulating the act of human randomized searching behaviors, a novel stochastic focusing search PSO (SFSPSO) is proposed. With dynamic neighborhood topology and subpopulation strategy, SFSPSO can improve the global searching ability while keeping the diversity and avoiding the astringency of a local extremum. The simulation results show that SFSPSO is competitive to solve various benchmark problems.
(2) A hierarchical subpopulation PSO (HSPSO) is presented to improve the convergence speed and accuracy by using the strategy of subpopulation hierarchy. The information is gradually flowed from the upper level to the lower level of the hierarchy, and the communication in the particle swarm is moderate and suitable for a proper balance between the global and local searching ability.
(3) A hierarchical chaos PSO (HCPSO) is proposed, which is based on the structure of the hierarchical multi-subpopulations. The novel algorithm adopts chaotic mutation in the nonlinear and decreasing inertia weight. The new global best position is the average position of several individuals that are picked out as exemplars when the new global best positions are updated in each dimension. The radius of the chaotic searching region can be adaptively adjusted. The simulation results show that HCPSO is more effective to overcome the slow convergence and prematurity.
The HSPSO has been applied in time-cost-quality synthesis optimization of the construction project. Satisfied results can be quickly obtained by using HSPSO with a smaller swarm. The exhaustive enumeration is given to verify the effectiveness of HSPSO.
Novel PSO algorithms are also applied to the antenna array synthesis, which include:
(1) The multiple subpopulation PSO (MSPSO) is presented in the pattern synthesis of equally spaced linear arrays. MSPSO is built by employing the strategy of hierarchy and subpopulation with the neighbor structure of von Neumann. A modified objective function model, which utilizes different fitness functions according to the character of the top and bottom layer, is proposed to balance the local and global searching ability. The simulation results show that it achieves relatively high performance and can avoid the premature and easily trapping in local optima.
(2) A chaotic search PSO (CSPSO) is proposed by fusing the advantages of both chaotic optimization algorithm and PSO. The novel algorithm utilizes chaotic searching strategy when the swarm is trapped into stagnancy. To enhance the diversity of samples, several individuals are picked out as exemplars when the new global best position is updated in each dimension. The simulation results show that it achieves relatively high performance by applying CSPSO in the pattern synthesis of antenna arrays with sidelobe reduction and null control.
(3) To deal with the pattern synthesis of the equally spaced linear array, unequally spaced linear array and conformal array, a chaotic PSO (OPSO) is presented. Experimental results present its high performance in the pattern synthesis with low sidelobe level, multi-nulls and shaped beam.
(4) The chaotic binary PSO (CBPSO) is presented as a useful alternative for the synthesis of thinned arrays. CBPSO is improved by nonlinear inertia weight with chaotic mutation to increase the diversity of particles. An extensive numerical analysis has been performed by addressing thinned linear and planar arrays with sidelobe suppression. Simulation results are proposed to compare with published results to verify the effectiveness of the proposed method.
引文
[1]陈宝林.最优化理论与算法(第2版).清华大学出版社,2005.
[2]周雪刚.非凸优化问题的全局优化算法.中南大学博士学位论文.2010.
[3]薛毅.最优化原理与方法.北京工业大学出版社,2001.
[4]邢立宁.知识型智能优化方法及其应用研究.国防科学技术大学博士学位论文.2009.
[5]E. Bonabeau, M. Dorigo, G. Theraulaz. Swarm intelligence:from natural to artificial systems. Oxford University Press,1999.
[6]R. C. Eberhart, Y.-H. Shi, J. Kennedy. Swarm intelligence. Morgan Kaufmann,2005.
[7]M. Dorigo, T. Stutzle. Ant colony optimization. The MIT Press,2004.
[8]J. Kennedy, R. C. Eberhart. Particle swarm optimization. Proceedings of the Conference on Neural Networks, Perth, Aust,1995:1942-1948.
[9]寇晓丽.群智能算法及其应用研究.西安电子科技大学博士学位论文.2009.
[10]莫愿斌.粒子群优化算法的扩展与应用.浙江大学博十学位论文.2006.
[11]张玮.粒子群优化算法研究及在阵列天线中的应用.太原理工大学博士学位论文.2010.
[12]R. Horst, P. M. Pardalos, N. V. Thoai.全局优化引论.清华大学出版社,2003.
[13]王凌.智能优化算法及其应用.清华大学出版社,2001.
[14]蔡宣三.最优化与最优控制.清华大学出版社,1982.
[15]L. C. W. Dixon, G. P. Szego. Towards global optimisation. North-Holland Publishing Company,1978.
[16]D. H. Wolpert, W. G. Macready. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation.1997,1(1):67-82.
[17]曾建潮,介婧,崔志华.微粒群算法.科学出版社,2004.
[18]刘建华.粒子群算法的基本理论及其改进研究.中南大学博士学位论文.2009.
[19]C. M. Fonseca, P. J. Fleming. An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation.1995,3(1):1-16.
[20]云庆夏.进化算法.冶金工业出版社,2000.
[21]王正志.进化计算.国防科技大学出版社,2000.
[22]W. Spears, K. De Jong, T. Back, D. Fogel, H. de Garis. An overview of evolutionary computation. Springer,1993.
[23]J. H. Holland. Adapatation in nature and artificial systems. The University of Michigan Press,1975.
[24]J. R. Koza. Genetic programming:on the programming of computers by natural selection. The MIT Press,1992.
[25]L. J. Forgel. On the organization of intellect. University of California Ph.D. dissertation.1964.
[26]J. Kennedy. Handbook of nature-inspired and innovative computing "inbook" swarm intelligenced. Springer,2006.
[27]T. Hendtlass, M. Randall. A survey of ant colony and particle swarm meta-heuristics and their application to discrete optimization problems. Proceedings of the Inaugural Workshop on Artificial Life, Adelaide, South Australia,2001:15-25.
[28]唐贤伦.混沌粒子群优化算法理论及应用研究.重庆大学博十学位论文.2007.
[29]C. W. Reynolds. Flocks, herds and schools:a distributed behavioral model. Computer Graphics (ACM).1987,21(4):25-34.
[30]A. Colorni, M. Dorigo, V. Maniezzo. An investigation of some properties of an ant algorithm. Proceedings of the Parallel Problem Solving, Brussels, Belg.,1992:509-520.
[31]M. Dorigo, V. Maniezzo, A. Colorni. Ant system:optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics.1996,26(1):29-41.
[32]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.
[33]J. J. Hopfield, D. W. Tank. "Neural" computation of decisions in optimization problems. Biological Cybernetics.1985,52(3):141-152.
[34]N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, E. Teller. Equation of state calculations by fast computing machines. The Journal of Chemical Physics.1953,21(6):1087-1092.
[35]S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi. Optimization by simmulated annealing. Science.1983, 220(4598):671-680.
[36]F. Glover, M. Laguna. Tabu search. Kluwer Academic Pub.,1998.
[37]J. D. Farmer, N. H. Packard, A. S. Perelson. The immune system, adaptation, and machine learning. Physica.1986,2(3):187-204.
[38]R. G. Reynolds. An introduction to cultural algorithms. World Scientific Publishing Co.,1994.
[39]W. H. Durham. Coevolution:genes, culture, and human diversity. Stanford University Press,1991.
[40]L. M. Adleman. Molecular computation of solutions to combinatorial problems. Science.1994, 266(5187):1021-1024.
[41]支凌迎,殷志祥,黄晓慧,胡娟.DNA计算研究概述与分析.系统工程与电子技术.2009,31(6)：1462-1466.
[42]P. A. Benioff. Quantum mechanical hamiltonian models of discrete processes that erase their own histories:application to turing machines. International Journal of Theoretical Physics.1982,21(3): 177-201.
[43]R. P. Feynman. Simulating physics with computers. International Journal of Theoretical Physics. 1982,21(6):467-488.
[44]D. Deutsch. Quantum theory, the church-turing principle and the universal quantum computer. Proceedings of the Royal Society of London-Series A:Mathematical and Physical Sciences.1985, 400(1818):97-117.
[45]E. Bernstein, U. Vazirani. Quantum complexity theory. SIAM J. Comput,1997.
[46]R. Storn, K. Price. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization.1997,11(4):341-359.
[47]R. Storn. Differential evolution design of an IIR-filter. IEEE Congress on Evolutionary Computation, Nayoya University, Japan,1996:268-273.
[48]F.-S. Wang, J.-P. Chiou. Optimal control and optimal time location problems of differential-algebraic systems by differential evolution. Industrial & Engineering Chemistry Research.1997,36(12): 5348-5357.
[49]A.-Y. Qing. Electromagnetic inverse scattering of multiple two-dimensional perfectly conducting objects by the differential evolution strategy. IEEE Transactions on Antennas and Propagation.2003, 51(6):1251-1262.
[50]C. Lin, A.-Y. Qing, Q.-Y. Feng. Synthesis of unequally spaced antenna arrays by using differential evolution. IEEE Transactions on Antennas and Propagation.2010,58(8):2553-2561.
[51]林川.粒子群优化与差分进化算法研究及其应用.西南交通大学博士学位论文.2009.
[52]S. Xu, R. S. Yahya. Boundary conditions in particle swarm optimization revisited. IEEE Transactions on Antennas and Propagation.2007,55(3):760-765.
[53]Y.-H. Shi, R. Eberhart. A modified particle swarm optimizer. IEEE Congress on Evolutionary Computation, Anchorage, AK, USA,1998:69-73.
[54]M. Clerc, J. Kennedy. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation.2002,6(1): 58-73.
[55]R. C. Eberhart, Y.-H. Shi. Comparing inertia weights and constriction factors in particle swarm optimization. IEEE Congress on Evolutionary Computation, California, CA, USA,2000:84-88.
[56]A. Carlisle, G. Dozier. An off-the-shelf PSO. Proceedings of the Workshop on Particle Swarm Optimization, Indianapolis, Indiana, USA,2001:201-206.
[57]D. J. Watts. Collective dynamics of 'small-world' networks. Nature.1998,393(6864):440-442.
[58]D. J. Watts. Small worlds:the dynamics of networks between order and randomness. Princeton University Press,2003.
[59]J. Kennedy, R. Mendes. Neighborhood topologies in fully informed and best-of-neighborhood particle swarms. IEEE Transactions on Systems, Man, and Cybernetics.2006,36(4):515-519.
[60]J. Kennedy. Small worlds and mega-minds:effects of neighborhood topology on particle swarm performance. IEEE Congress on Evolutionary Computation, Washington, DC, USA,1999: 1931-1938.
[61]R. Mendes. Population topologies and their influence in particle swarm performance. University of Minho Ph.D. dissertation.2004.
[62]王雪飞.粒子群算法的动态拓朴结构研究.西南大学博士学位论文.2008.
[63]R. Poli, J. Kennedy, T. Blackwell. Particle swarm optimization. Swarm Intelligence.2007,1(1): 33-57.
[64]E. Ozcan, C. K. Mohan. Particle swarm optimization:surfing the waves. IEEE Congress on Evolutionary Computation, Washington, D.C., USA,1999:1944-1953.
[65]I. C. Trelea. The particle swarm optimization algorithm:convergence analysis and parameter selection. Information Processing Letters.2003,85(6):317-325.
[66]F. J. Solis, R. J. B. Wets. Minimization by random search techniques. Mathematics of Operations Research.1981,6(1):19-30.
[67]F. van den Bergh, A. P. Engelbrecht. A study of particle swarm optimization particle trajectories. Information Sciences.2006,176(8):937-971.
[68]V. Kadirkamanathan, K. Selvarajah, P. J. Fleming. Stability analysis of the particle dynamics in particle swarm optimizer. IEEE Transactions on Evolutionary Computation.2006,10(3):245-255.
[69]R. Poli, W. Langdon, M. Clerc, C. Stephens. Foundations of genetic algorithms. Springer Berlin Heidelberg,2007.
[70]潘峰,陈杰,甘明刚,蔡涛,涂序彦.粒子群优化算法模型分析.自动化学报.2006,32(3)：368-377.
[71]李宁,孙德宝,邹彤,秦元庆,尉宇.基于差分方程的PSO算法粒子运动轨迹分析.计算机学报.2006,29(11)：2052-2060.
[72]H. Liu, A. Abraham, M. Clerc. Chaotic dynamic characteristics in swarm intelligence. Applied Soft Computing.2007,7(3):1019-1026.
[73]Y.-H. Shi, R. C. Eberhart. Fuzzy adaptive particle swarm optimization. IEEE Congress on Evolutionary Computation, Seoul, South Korea,2001:101-106.
[74]A. Ratnaweera, S. K. Halgamuge, H. C. Watson. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation.2004,8(3):240-255.
[75]林川,冯全源.一种新的自适应粒子群优化算法.计算机工程.2008,34(7)：181-183.
[76]A.-K. Buthainah, K. M. Chilukuri. Multi-phase generalization of the particle swarm optimization algorithm. IEEE Congress on Evolutionary Computation, Honolulu, HI, USA,2002:489-494.
[77]F. van den Bergh, A. P. Engelbrecht. A new locally convergent particle swarm optimiser. IEEE Conference on Systems, Man and Cybernetics, Hammamet, Tunisia,2002:184-190.
[78]T. Peram, K. Veeramachaneni, C. K. Mohan. Fitness-distance-ratio based particle swarm optimization. Proceedings of the IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA,2003:174-181.
[79]J.-J. Liang, A.-K. Qin, P. N. Suganthan, S. Baskar. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation. 2006,10(3):281-295.
[80]Y. Chunming, D. Simon. A new particle swarm optimization technique.18th International Conference on Systems Engineering, Las Vegas, NV, USA,2005:164-169.
[81]A. I. Selvakumar, K. Thanushkodi. A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Transactions on Power Systems.2007,22(1):42-51.
[82]S. He, Q.-H. Wu, J.-Y. Wen, J. R. Saunders, R. C. Paton. A particle swarm optimizer with passive congregation. Biosystems.2004,78(3):135-147.
[83]W. Jian, Y. Xue, J. Huang. Combined algorithm for time-varying system based on improved particle swarm optimization and EWRLS algorithm. Proceedings of the IEEE International Conference on Industrial Technology, Hammamet, Tunisia,2004:1474-1477
[84]孟红记,郑鹏,梅国晖,谢植.基于混沌序列的粒子群优化算法.控制与决策.2006,21(3)：263-266.
[85]J. Kennedy. Stereotyping:improving particle swarm performance with cluster analysis. IEEE Congress on Evolutionary Computation, La Jolla, CA, USA,2000:1507-1512.
[86]R. Mendes, J. Kennedy, J. Neves. The fully informed particle swarm:simpler, maybe better. IEEE Transactions on Evolutionary Computation.2004,8(3):204-210.
[87]F. van den Bergh, A. P. Engelbrecht. Training product unit networks using cooperative particle swarm optimisers. International Joint Conference on Neural Networks, Vienna, Austria,2001:126-131.
[88]P. N. Suganthan. Particle swarm optimiser with neighbourhood operator. IEEE Congress on Evolutionary Computation, Washington, DC, USA,1999:1958-1962.
[89]B. Niu, Y. Zhu, X. He, H. Wu. MCPSO:a multi-swarm cooperative particle swarm optimizer. Applied Mathematics and Computation.2007,185(2):1050-1062.
[90]E. A. Mohammed, K. Mohamed. Cooperative particle swarm optimizers:a powerful and promising approach. Stigmergic Optimization.2006,31:239-259.
[91]S. Selleri, M. Mussetta, P. Pirinoli, R. E. Zich, L. Matekovits. Some insight over new variations of the particle swarm optimization method. Antennas and Wireless Propagation Letters.2006,5(1): 235-238.
[92]X.-H. Shi, Y.-H. Lu, C.-G. Zhou, H.-P. Lee, W.-Z. Lin, Y.-C. Liang. Hybrid evolutionary algorithms based on PSO and GA. IEEE Congress on Evolutionary Computation, Canberra, Australia,2003: 2393-2399.
[93]P. J. Angeline. Using selection to improve particle swarm optimization. IEEE World Congress on Computational Intelligence Evolutionary Computation Proceedings, Anchorage, AK, USA,1998: 84-89.
[94]W. H. Duminy, A. P. Engelbrecht. Tournament particle swarm optimization. IEEE Symposium on Computational Intelligence and Games, Honolulu, HI, USA,2007:146-153.
[95]B. Yang, Y. Chen, Z. Zhao. A hybrid evolutionary algorithm by combination of PSO and GA for unconstrained and constrained optimization problems. IEEE Conference on Control and Automation, Wuhan Univ., Wuhan,2007:166-170.
[96]郑小霞,钱锋.一种改进的微粒群优化算法.计算机工程.2006,32(15)：25-27.
[97]T. Hendtlass. Engineering of intelligent systems lecture notes in computer science. Springer,2001.
[98]A. Kaveh, S. Talatahari. Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Computers & Structures.2009,87(5):267-283.
[99]窦全胜,周春光,马铭.粒子群优化的两种改进策略.计算机研究与发展.2005,42(5)：879-904.
[100]N. Higashi, H. Iba. Particle swarm optimization with gaussian mutation. Swarm Intelligence Symposium, Indianapolis, Indiana, USA,2003:72-79.
[101]L. Morten, R. Thomas Kiel, K. Thiemo. Hybrid particle swarm optimiser with breeding and subpopulations. Proceedings of the Third Genetic Algorithm, San Francisco, California, USA,2001: 469-476.
[102]B. Liu, L. Wang, Y.-H. Jin, F. Tang, D.-X. Huang. Improved particle swarm optimization combined with chaos. Chaos, Solitons and Fractals.2005,25(5):1261-1271.
[103]费春国,韩正之.一种改进的混沌优化算法.控制理论与应用.2006,23(3)：471-474.
[104]J. Kennedy, R. C. Eberhart. A discrete binary version of the particle swarm algorithm. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Orlando, FL, USA,1997: 4104-4108.
[105]崔志华,曾建潮.基于控制理论的微粒群算法分析与改进.小型微型计算机系统.2006,27(5)：1333-1338.
[106]A. Acan, A. Unveren, M. Bodur. The latest vs. averaged recent experience:which better guides a PSO algorithm? IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada,2006:414-419.
[107]T. Ray, K. Liew. A swarm metaphor for multiobjective design optimization. Engineering Optimization. 2002,34(2):141-153.
[108]K. E. Parsopoulos, M. N. Vrahatis. Particle swarm optimization method for constrained optimization problems. Proceedings of the Euro-International Symposium on Computational Intelligence, Shfeim, Israel,2002:214-220.
[109]M. Abido. Optimal design of power-system stabilizers using particle swarm optimization. IEEE Transactions on Energy Conversion.2002,17(3):406-413.
[110]H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, Y. Nakanishi. A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Transactions on Power Systems.2000,15(4):1232-1239.
[111]J.-R. Zhang, J. Zhang, T. M. Lok, M. R. Lyu. A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation.2007, 185(2):1026-1037.
[112]X. Zhang, L. Yu, Y. Zheng. Two-stage adaptive PMD compensation in a 10 Gbit/s optical communication system using particle swarm optimization algorithm. Optics Communications.2004, 231(6):233-242.
[113]H.-W. Liu, J. Li. A particle swarm optimization-based multiuser detection for receive-diversity-aided STBC systems. Signal Processing Letters.2008,15:29-32.
[114]T. K. Rasmussen, T. Krink. Improved hidden markov model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid. Biosystems.2003,72(2):5-17.
[115]X. Xiao, E. R. Dow, R. Eberhart, B. Z. Miled, R. J. Oppelt. A hybrid self-organizing maps and particle swarm optimization approach. Concurrency and Computation:Practice and Experience.2004,16(9): 895-915.
[116]W.-T. Li, X.-W. Shi, Y.-Q. Hei, S.-F. Liu, J. Zhu. A hybrid optimization algorithm and its application for conformal array pattern synthesis. IEEE Transactions on Antennas and Propagation.2010,58(10): 3401-3406.
[117]S. K. Goudos, V. Moysiadou, T. Samaras, K. Siakavara, J. N. Sahalos. Application of a comprehensive learning particle swarm optimizer to unequally spaced linear array synthesis with sidelobe level suppression and null control. Antennas and Wireless Propagation Letters.2010,9: 125-129.
[118]T. H. Ismail, Z. M. Hamici. Array pattern synthesis using digital phase control by quantized particle swarm optimization. IEEE Transactions on Antennas and Propagation.2010,58(6):2142-2145.
[119]金荣洪,袁智皓,耿军平,范瑜,李佳靖.基于改进粒子群算法的天线方向图综合技术.电波科学学报.2006,21(6)：873-878.
[120]Z. L. Gaing. A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Transactions on Energy Conversion.2004,19(2):384-391.
[121]A. E. M. Zavala, E. R. V. Diharce, A. H. Aguirre. Particle evolutionary swarm for design reliability optimization. Lecture Notes in Computer Science.2005,3410:856-869.
[122]A. Elwer, S. Wahsh, M. Khalil, A. Nur-Eldeen. Intelligent fuzzy controller using particle swarm optimization for control of permanent magnet synchronous motor for electric vehicle. The 29th Annual Conference of the IEEE Industrial Electronics Society, Roanoke, VA, USA,2003: 1762-1766
[123]V. G. Gudise, G. K. Venayagamoorthy. FPGA placement and routing using particle swarm optimization. IEEE Computer Society Annual Symposium on VLSI:Emerging Trends in VLSI Systems Design, Lafayette, LA, USA,2004:307-308.
[124]张宝.粒子群算法及其在卫星舱布局中的应用研究.大连理工大学博士学位论文.2006.
[125]W. Zhong, J. Zhang, W. Chen. A novel discrete particle swarm optimization to solve traveling salesman problem. IEEE Congress on Evolutionary Computation, Singapore,2007:3283-3287.
[126]C. A. C. Coello, G. T. Pulido, M. S. Lechuga. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation.2004,8(3):256-279.
[127]M. G. H. Omran, A. Salman, A. P. Engelbrecht. Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Analysis & Applications.2006,8(4):332-344.
[128]K. E. Parsopoulos, E. I. Papageorgiou, P. P. Groumpos, M. N. Vrahatis. Evolutionary computation techniques for optimizing fuzzy cognitive maps in radiation therapy systems. Genetic and Evolutionary Computation, Seattle, Washington, USA,2004:402-413.
[129]S. Das, A. Abraham, A. Konar. Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. Pattern Recognition Letters.2008,29(5):688-699.
[130]C. O. Ourique, E. C. Biscaia. The use of particle swarm optimization for dynamical analysis in chemical processes. Computers & Chemical Engineering.2002,26(12):1783-1793.
[131]G. K. Venayagamoorthy, S. Doctor. Navigation of mobile sensors using PSO and embedded P SO in a fuzzy logic controller.39th IAS Annual Meeting Industry Applications Conference, Rolla, MO, USA, 2004:1200-1206
[132]T. Ray, K. M. Liew. A swarm with an effective information sharing mechanism for unconstrained and constrained single objective optimisation problems. IEEE Congress on Evolutionary Computation, Seoul, South Korea,2001:75-80
[133]X.-H. Hu, R. Eberhart. Solving constrained nonlinear optimization problems with particle swarm optimization.6th World Multiconference on Systemics, Cybernetics and Informatics, Orlando, Florida, USA,2002:203-206.
[134]Q. He, L. Wang. A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Applied Mathematics and Computation.2007,186(2):1407-1422.
[135]J. Kennedy, W. M. Spears. Matching algorithms to problems:an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. IEEE World Congress on Computational Intelligence Evolutionary Computation Proceedings, Anchorage, AK, USA,1998: 78-83.
[136]X.-H. Hu, R. C. Eberhart, Y.-H. Shi. Swarm intelligence for permutation optimization:a case study of n-queens problem. Proceedings of the IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA,2003:243-246.
[137]M. Clerc. New optimization techniques in engineering. Springer-Verlag,2004.
[138]G. Pampara, N. Franken, A. Engelbrecht. Combining particle swarm optimisation with angle modulation to solve binary problems. IEEE Congress on Evolutionary Computation, Edinburgh, Scotland,2005:89-96.
[139]K. E. Parsopoulos, M. N. Vrahatis. Particle swarm optimization method in multiobjective problems. ACM symposium, Madrid, Spain,2002:603-607.
[140]G. T. Pulido, C. A. Coello. Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer. Genetic and Evolutionary Computation, Seattle, Washington, USA,2004:225-237.
[141]X.-D. Li. A non-dominated sorting particle swarm optimizer for multiobjective optimization. Genetic and Evolutionary Computation, Chicago, II, USA,2003:198-198.
[142]王维博,林川,郑永康.粒子群算法中参数的实验与分析.西华大学学报(自然科学版).2008,27(1)：76-80.
[143]Y.-H. Shi, R. C. Eberhart. Parameter selection in particle swarm optimization. Proceedings of the 7th International Conference on Evolutionary Programming, San Diego, CA, USA,1998:591-600.
[144]Y.-H. Shi, R. C. Eberhart. Empirical study of particle swarm optimization. IEEE Congress on Evolutionary Computation, Washington, D.C., USA,1999:1945-1950.
[145]J. Robinson, R.-S. Yahya. Particle swarm optimization in electromagnetics. IEEE Transactions on Antennas and Propagation.2004,52(2):397-407.
[146]T. Huang, A. S. Mohan. A hybrid boundary condition for robust particle swarm optimization. Antennas and Wireless Propagation Letters.2005,4:112-117.
[147]X. Yao, Y. Liu, G. Lin. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation.1999,3(2):82-102.
[148]王俊伟,汪定伟.粒子群算法中惯性权重的实验与分析.系统工程学报.2005,20(2)：194-198.
[149]L.-P. Zhang, H.-J. Yu, S.-X. Hu. Optimal choice of parameters for particle swarm optimization. Journal of Zhejiang University-Science A.2005,6(6):528-534.
[150]W.-B. Wang, Q.-Y. Feng, Y.-K. Zheng. A novel particle swarm optimization algorithm with stochastic focusing search for real-parameter optimization. 11th IEEE Singapore International Conference on Communication Systems, Guangzhou, China,2008:583-587.
[151]W.-B. Wang, Q.-Y. Feng. A hierarchical particle swarm optimization algorithm combined with chaotic search. International Conference on Computer and Communication Technologies in Agriculture Engineering, Chengdu, China,2010:435-438.
[152]王维博,冯全源.基于分层多子群的混沌粒子群优化算法.控制与决策.2010,25(11)：1663-1668.
[153]M. H. Wright. Direct search methods:once scorned, now respectable. Proceedings of the Dundee Biennial Conference in Numerical analysis, Harlow, UK,1995:191-208.
[154]M. W. Trosset. I know it when I see it:toward a definition of direct search methods. Citeseer,1997.
[155]K. Penev, G. Littlefair. Free search-a comparative analysis. Information Sciences.2005,172(1): 173-193.
[156]E. Burke, G. Kendall, J. Newall, E. Hart, P. Ross, S. Schulenburg. Hyper-heuristics:an emerging direction in modern search technology. Springer,2003.
[157]S. J. Russell, P. Norvig, J. F. Candy, J. M. Malik, D. D. Edwards. Artificial intelligence:a modern approach. Prentice Hall,2010.
[158]高波,田东,胡亮,傅泽田.Free Search—一种新的进化算法.仪器仪表学报.2006,27(Z1)：819-821.
[159]李奇,陈维荣,段涛,刘述奎,刘元勋.基于随机聚焦粒子群算法的多机系统PSS参数优化.电力系统保护与控制.2009,37(13)：13-18.
[160]郑永康,陈维荣,戴朝华,王维博.随机聚焦搜索优化算法.控制理论与应用.2009,26(8)：915-917.
[161]C.-F. Juang, C.-M. Hsiao, C.-H. Hsu. Hierarchical cluster-based multispecies particle-swarm optimization for fuzzy-system optimization. IEEE Transactions on Fuzzy Systems.2010,18(1):14-26.
[162]S. Janson, M. Middendorf. A hierarchical particle swarm optimizer and its adaptive variant. IEEE Transactions on Systems, Man, and Cybernetics.2005,35(6):1272-1282.
[163]S. Janson, M. Middendorf. A hierarchical particle swarm optimizer for noisy and dynamic environments. Genetic Programming and Evolvable Machines.2006,7(4):329-354.
[164]C.-C. Chen. Two-layer particle swarm optimization for unconstrained optimization problems. Applied Soft Computing.2011,11(1):295-304.
[165]C.-F. Juang. A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Transactions on Systems, Man, and Cybernetics.2004,34(2):997-1006.
[166]C. Lin, Q.-Y. Feng. A hierarchical subpopulation particle swarm optimization algorithm. Intelligent Systems and Knowledge Engineering, Chengdu, Sichuan, China,2007:1487-1491.
[167]宋莹.基于混沌和粒子群优化的非线性建模与控制研究.南开大学博十学位论文.2007.
[168]T.-Y. Li, J. A. Yorke. Period three implies chaos. American Mathematical Monthly.1975,82(10): 985-992.
[169]R. M. May. Simple mathematical models with very complicated dynamics. Nature.1976,261(5560): 459-467.
[170]M. J. Feigenbaum. Quantitative universality for a class of nonlinear transformations. Journal of Statistical Physics.1978,19(1):25-52.
[171]李兵,蒋慰孙.混沌优化方法及其应用.控制理论与应用.1997,14(4)：613-615.
[172]B. Li, W.-S. Jiang. Optimizing complex functions by chaos search. Cybernetics and Systems.1998, 29(4):409-419.
[173]张彤,王宏伟,王子才.变尺度混沌优化方法及其应用.控制与决策.1999,14(3)：285-288.
[174]T. Kwok, K. A. Smith. A unified framework for chaotic neural-network approaches to combinatorial optimization. IEEE Transactions on Neural Networks.1999,10(4):978-981.
[175]T. Tanaka, E. Hiura. Computational abilities of a chaotic neural network. Physics Letters A.2003, 315(3):225-230.
[176]C.-S. Zhou, T.-L. Chen, W.-Q. Huang. Chaotic neural network with nonlinear self-feedback and its application in optimization. Neurocomputing.1997,14(3):209-222.
[177]尤勇,王孙安,盛万兴.新型混沌优化方法的研究及应用.西安交通大学学报.2003,37(1)：69-72.
[178]单梁,强浩,李军,王执铨.基于Tent映射的混沌优化算法.控制与决策.2005,20(2)：179-182.
[179]Q. Wu. A self-adaptive embedded chaotic particle swarm optimization for parameters selection of Wv-S VM. Expert Systems with Applications.2011,38(1):184-192.
[180]Q. Tao, H.-Y. Chang, Y. Yi, C.-Q. Gu, W.-J. Li. A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow. Computers and Operations Research.2011,38(5):824-836.
[181]L.-Y. Chuang, C.-H. Yang, J.-C. Li. Chaotic maps based on binary particle swarm optimization for feature selection. Applied Soft Computing.2011,11(1):239-248.
[182]H.-B. Zhao, S.-D. Yin. A CPSO-SVM model for ultimate bearing capacity determination. Marine Georesources and Geotechnology.2010,28(1):64-75.
[183]Y. Wang, J.-Z. Zhou, H. Qin, Y. Lu. Improved chaotic particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects. Energy Conversion and Management. 2010,51(12):2893-2900.
[184]J. B. Park, Y. W. Jeong, J. R. Shin, K. Y. Lee. An improved particle swarm optimization for nonconvex economic dispatch problems. IEEE Transactions on Power Systems.2010,25(1):156-166.
[185]雷德明.利用混沌搜索全局最优解的一种混合遗传算法.系统工程与电子技术.1999,21(12)：81-82.
[186]X.-F. Zou, M. Wang, A. Zhou, B. Mckay. Evolutionary optimization based on chaotic sequence in dynamic environments. IEEE Conference on Networking, Sensing and Control, Taipei, Taiwan,2004: 1364-1369.
[187]李亚东,李少远.一种新的遗传混沌优化组合方法.控制理论与应用.2002,19(1)：143-145.
[188]L. Wang, K. Smith. On chaotic simulated annealing. IEEE Transactions on Neural Networks 1998, 9(4):716-718.
[189]徐宁,州尚波,张红民,虞厥邦.一种混合混沌优化方法及其应用.系统工程与电子技术.2003,25(2)：226-227+244.
[190]张志新,张明廉.基于并行混沌和单纯形法的混合全局优化算法.系统仿真学报.2004,16(1)：35-37.
[191]林川,冯全源.基于微粒群本质特征的混沌微粒群优化算法.西南交通大学学报.2007,42(6)：665-669.
[192]X. Tao, X.-F. Liao, K.-W. Wong. An improved particle swarm optimization algorithm combined with piecewise linear chaotic map. Applied Mathematics and Computation.2007,190(2):1637-1645.
[193]J. B. Park, Y. W. Jeong, W. N. Lee, J. R. Shin. An improved particle swarm optimization for economic dispatch problems with non-smooth cost functions. Power Engineering Society General Meeting, Montreal, Quebec, Canada,2006:121-126.
[194]J. Kennedy. In search of the essential particle swarm. IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada,2006:1694-1701.
[195]丁鑫鑫,唐常杰,曾涛,张培颂,徐开阔,刘齐宏.基于最佳粒子共享和分层搜索的并行粒子群优化算法.四川大学学报(自然科学版).2008,45(4)：799-803.
[196]范九伦,张雪锋.分段Logistic混沌映射及其性能分析.电子学报.2009,37(4)：720-725.
[197]C.-W. Jiang, E. Bompard. A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation. Mathematics and Computers in Simulation.2005,68(1):57-65.
[198]王巍,彭力.嵌入隔离小生境技术的混沌粒子群算法.系统工程与电子技术.2008,30(6)：1151-1154.
[199]吕振肃,侯志荣.自适应变异的粒子群优化算法.电子学报.2004,32(3)：416-420.
[200]陈如清,俞金寿.混沌粒子群混合优化算法的研究与应用.系统仿真学报.2008,20(3)：685-688.
[201]A. Chatterjee, P. Siarry. Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers and Operations Research.2006,33(3):859-871.
[202]王维博,冯全源.基于改进粒子群算法的工程项目综合优化.西南交通大学学报.2011,46(1)：76-83.
[203]成虎.工程项目管理.中国建筑共业出版社,2009.
[204]符志民.项目管理理论与实践.中国宇航出版社,2002.
[205]范黎波.项目管理.对外贸易大学出版社,2005.
[206]杨坤.项目时间管理.南开大学出版社,2006.
[207]朱宏亮.项目进度管理.清华大学出版社,2002.
[208]周艳.工程项目工期成本质量综合优化研究.西南交通大学硕士学位论文.2008.
[209]R. McKim, T. Hegazy, M. Attalla. Project performance control in reconstruction projects. Journal of Construction Engineering and Management.2000,126(2):137-141.
[210]P. Rwelamila, K. Hall. Total systems intervention:an integrated approach to time, cost and quality management. Construction Management and Economics.1995,13(3):235-241.
[211]S. W. Hsiao. Fuzzy logic based decision model for product design. International Journal of Industrial Ergonomics.1998,21(2):103-116.
[212]熊鹰,匡亚萍.基于蚁群算法的施工项目工期—成本优化.系统工程理论与实践.2007,27(3)：105-111.
[213]王健,刘尔烈,骆刚.工程项目管理中工期—成本—质量综合均衡优化.系统工程学报.2004,19(2)：148-153.
[214]杨耀红,汪应洛,王能民.工程项目工期成本质量模糊均衡优化研究.系统工程理论与实践.2006,26(7)：112-117.
[215]高兴夫,胡程顺,钟登华.工程项目管理的工期一费用—质量综合优化研究.系统工程理论与实践.2007,27(10)：112-117.
[216]E.-R. Khaled, K. Amr. Time-cost-quality trade-off analysis for highway construction. Journal of Construction Engineering and Management.2005,131(4):477-486.
[217]骆刚,刘尔烈,王健.遗传算法在网络计划资源优化中的应用.天津大学学报.2004,37(2)：179-183.
[218]刘晓峰,陈通,张连营.基于微粒群算法的工程项目质量、费用和工期综合优化.土木工程学报.2006,39(10)：122-126.
[219]陈志勇,杜志达,周华.基于微粒群算法的工程项目资源均衡优化.土木工程学报.2007,40(2)：93-96.
[220]R. Storn, K. Price. Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science.1995,7(12):1-12.
[221]刘鸣,袁超伟,贾宁,黄韬.智能天线的技术与应用.机械工业出版社,2007.
[222]C. K. Ng, N. K. Noordin, S. Khatun, B. M. Ali, S. S. Jamuar, M. Ismail. Directional diversity of smart antenna in LAS CDMA systems. Wireless Personal Communications.2008,46(3):305-316.
[223]尚飞,蔡亚星,张颖,高本庆.阵列天线的双种群遗传算法综合.电波科学学报.2007,1l(2)：224-228.
[224]C. B. Dietrich, W. L. Stutzman, B. K. Kim, K. Dietze. Smart antennas in wireless communications: base-station diversity and handset beamforming. Antennas and Propagation Magazine.2000,42(5): 142-151.
[225]Z. Zaharis. Radiation pattern shaping of a mobile base station antenna array using a particle swarm optimization based technique. Electrical Engineering.2008,90(4):301-311.
[226]杨林.阵列天线综合方法研究.哈尔滨工程大学博士学位论文.2006.
[227]叶剑锋.基于免疫优化算法的阵列天线综合的研究.哈尔滨工程大学博十学位论文.2009.
[228]王维博,冯全源.粒子群优化算法在天线方向图综合中的应用.电子科技大学学报.2011,40(2)：237-241.
[229]王维博,冯全源.粒子群算法在阵列天线方向图综合中的应用.西安电子科技大学学报.2011,38(3)：175-180.
[230]W.-B. Wang, Q.-Y. Feng, D. Liu. Application of chaotic particle swarm optimization algorithm to pattern synthesis of antenna arrays. Progress in Electromagnetics Research.2011,115:173-189.
[231]W.-B. Wang, Q.-Y. Feng, D. Liu. Synthesis of thinned linear and planar antenna arrays using binary PSO algorithm. Progress in Electromagnetics Research.2012,127:371-387.
[232]金荣洪,耿军平,范瑜.无线通信中的智能天线.北京邮电大学出版社,2006.
[233]吕善伟.天线阵综合.北京航空学院出版社,1988.
[234]N.-B. Jin, R.-S. Yahya. Advances in particle swarm optimization for antenna designs:real-number, binary, single-objective and multiobjective implementations. IEEE Transactions on Antennas and Propagation.2007,55(3):556-567.
[235]D. G Kurup, M. Himdi, A. Rydberg. Synthesis of uniform amplitude unequally spaced antenna arrays using the differential evolution algorithm. IEEE Transactions on Antennas and Propagation.2003, 51(9):2210-2217.
[236]C. Dolph. A current distribution for broadside arrays which optimizes the relationship between beam width and side-lobe level. Proceedings of the IRE.1946,34(6):335-348.
[237]T. Taylor. Design of circular apertures for narrow beamwidth and low sidelobes. IRE Transactions on Antennas and Propagation.1960,8(1):17-22.
[238]B. P. Kumar, G. Branner. Design of unequally spaced arrays for performance improvement. IEEE Transactions on Antennas and Propagation.1999,47(3):511-523.
[239]B. Kumar, G. Branner. Generalized analytical technique for the synthesis of unequally spaced arrays with linear, planar, cylindrical or spherical geometry. IEEE Transactions on Antennas and Propagation.2005,53(2):621-634.
[240]J. M. Johnson, Y. Rahmat-Samii. Genetic algorithm optimization and its application to antenna design. Antennas and Propagation Society International Symposium, Seattle, WA, USA,1994:326-329
[241]R. L. Haupt. Thinned arrays using genetic algorithms. IEEE Transactions on Antennas and Propagation.1994,42(7):993-999.
[242]K.-K. Yan, Y. Lu. Sidelobe reduction in array-pattern synthesis using genetic algorithm. IEEE Transactions on Antennas and Propagation.1997,45(7):1117-1122.
[243]A. Tennant, M. Dawoud, A. Anderson. Array pattern nulling by element position perturbations using a genetic algorithm. Electronics Letters.1994,30(3):174-176.
[244]叶剑锋,王玉峰,庞伟正.多映射混沌免疫算法及其在天线阵方向图综合中的应用.哈尔滨工程大学学报.2007,28(11)：1273-1277.
[245]姚昆,杨万麟.最佳稀布直线阵列的分区动态规划法.电子学报.1994,22(2)：87-90.
[246]V. Murino, A. Trucco, C. S. Regazzoni. Synthesis of unequally spaced arrays by simulated annealing. IEEE Transactions on Signal Processing.1996,44(1):119-122.
[247]范瑜,金荣洪,耿军平,刘波.基于差分进化算法和遗传算法的混合优化算法及其在阵列天线方向图综合中的应用.电子学报.2004,32(12)：1997-2000.
[248]Y.-K. Chen, S.-W. Yang, Z.-P. Nie. The application of a modified differential evolution strategy to some array pattern synthesis problems. IEEE Transactions on Antennas and Propagation.2008,56(7): 1919-1927.
[249]高波,金荣洪,耿军平.基于一种新的粒子群算法的天线方向图综合技术.电子与信息学报.2007,29(12)：3015-3019.
[250]焦永昌,杨科,陈胜兵,张福顺.粒子群优化算法用于阵列天线方向图综合设计.电波科学学报.2006,21(1)：16-20.
[251]K. T. Chaturvedi, M. Pandit, L. Srivastava. Self-organizing hierarchical particle swarm optimization for nonconvex economic dispatch. IEEE Transactions on Power Systems.2008,8(3):1079-1087.
[252]J. Kennedy, R. Mendes. Population structure and particle swarm performance. IEEE Congress on Evolutionary Computation, Honolulu, HI, USA,2002:1671-1676.
[253]范瑜,金荣洪.基于一种新的遗传算法的天线方向图综合技术.电波科学学报.2004,19(2)：182-186.
[254]刘道华,原思聪,兰洋,马新建.混沌映射的粒子群优化方法.西安电子科技大学学报.2010,37(4)：764-769.
[255]张顶学,廖锐全.一种基于种群速度的自适应粒子群算法.控制与决策.2009,24(8)：1257-1260.
[256]H. Modares, A. Alfi, M.-M. Fateh. Parameter identification of chaotic dynamic systems through an improved particle swarm optimization. Expert Systems with Applications.2010,37(5):3714-3720.
[257]Y.-Y. He, J.-Z. Zhou, X.-Q. Xiang, H. Chen, H. Qin. Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling. Chaos, Solitons and Fractals.2009,42(5):3169-3176.
[258]J. R. Perez, J. Basterrechea. Hybrid particle swarm-based algorithms and their application to linear array synthesis. Progress in Electromagnetics Research.2009,90:63-74.
[259]刘东,冯全源.基于停滞检测粒了群算法的阵列天线方向图综合.电波科学学报.2009,24(4)： 697-701.
[260]M. M. Khodier, C. G. Christodoulou. Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization. IEEE Transactions on Antennas and Propagation. 2005,53(8):2674-2679.
[261]J. A. Ferreira, F. Ares. Pattern synthesis of conformal arrays by the simulated annealing technique. Electronics Letters.1997,33(14):1187-1189.
[262]B. Fuchs. Shaped beam synthesis of arbitrary arrays via linear programming. Antennas and Wireless Propagation Letters.2010,9:481-484.
[263]H.-J. Zhou, B.-H. Sun, J.-F. Li, Q.-Z. Liu. Efficient optimization and realization of a shaped-beam planar array for very large array application. Progress in Electromagnetics Research.2009,89:1-10.
[264]陈客松.稀布天线阵列的优化布阵技术研究.电子科技大学博十学位论文.2006.
[265]Z. D. Zaharis, T. V. Yioultsis. A novel adaptive beamforming technique applied on linear antenna arrays using adaptive mutated boolean PSO. Progress in Electromagnetics Research.2011,117: 165-179.
[266]G. Oliveri, L. Manica, A. Massa. ADS-based guidelines for thinned planar arrays. IEEE Transactions on Antennas and Propagation.2010,58(6):1935-1948.
[267]S. K. Goudos, K. Siakavara, T. Samaras, E. E. Vafiadis, J. N. Sahalos. Sparse linear array synthesis with multiple constraints using differential evolution with strategy adaptation. Antennas and Wireless Propagation Letters.2011,10:670-673.
[268]K.-S. Chen, Z.-S. He, C.-L. Han. A modified real GA for the sparse linear array synthesis with multiple constraints. IEEE Transactions on Antennas and Propagation.2010,58(11):3770-3770.
[269]L. Zhang, Y.-C. Jiao, Z.-B. Weng, F.-S. Zhang. Design of planar thinned arrays using a boolean differential evolution algorithm. IETMicrowaves, Antennas & Propagation.2010,4(12):2172-2178.
[270]G. K. Mahanti, N. Pathak, P. Mahanti. Synthesis of thinned linear antenna arrays with fixed sidelobe level using real-coded genetic algorithm. Progress in Electromagnetics Research.2007,75:319-328.
[271]O. Quevedo-Teruel, E. Rajo-Iglesias. Ant colony optimization in thinned array synthesis with minimum sidelobe level. Antennas and Wireless Propagation Letters.2006,5(1):349-352.
[272]K.-S. Chen, X.-H. Yun, Z.-S. He, C.-L. Han. Synthesis of sparse planar arrays using modified real genetic algorithm. IEEE Transactions on Antennas and Propagation.2007,55(4):1067-1073.
[2]周雪刚.非凸优化问题的全局优化算法.中南大学博士学位论文.2010.
[3]薛毅.最优化原理与方法.北京工业大学出版社,2001.
[4]邢立宁.知识型智能优化方法及其应用研究.国防科学技术大学博士学位论文.2009.
[5]E. Bonabeau, M. Dorigo, G. Theraulaz. Swarm intelligence:from natural to artificial systems. Oxford University Press,1999.
[6]R. C. Eberhart, Y.-H. Shi, J. Kennedy. Swarm intelligence. Morgan Kaufmann,2005.
[7]M. Dorigo, T. Stutzle. Ant colony optimization. The MIT Press,2004.
[8]J. Kennedy, R. C. Eberhart. Particle swarm optimization. Proceedings of the Conference on Neural Networks, Perth, Aust,1995:1942-1948.
[9]寇晓丽.群智能算法及其应用研究.西安电子科技大学博士学位论文.2009.
[10]莫愿斌.粒子群优化算法的扩展与应用.浙江大学博十学位论文.2006.
[11]张玮.粒子群优化算法研究及在阵列天线中的应用.太原理工大学博士学位论文.2010.
[12]R. Horst, P. M. Pardalos, N. V. Thoai.全局优化引论.清华大学出版社,2003.
[13]王凌.智能优化算法及其应用.清华大学出版社,2001.
[14]蔡宣三.最优化与最优控制.清华大学出版社,1982.
[15]L. C. W. Dixon, G. P. Szego. Towards global optimisation. North-Holland Publishing Company,1978.
[16]D. H. Wolpert, W. G. Macready. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation.1997,1(1):67-82.
[17]曾建潮,介婧,崔志华.微粒群算法.科学出版社,2004.
[18]刘建华.粒子群算法的基本理论及其改进研究.中南大学博士学位论文.2009.
[19]C. M. Fonseca, P. J. Fleming. An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation.1995,3(1):1-16.
[20]云庆夏.进化算法.冶金工业出版社,2000.
[21]王正志.进化计算.国防科技大学出版社,2000.
[22]W. Spears, K. De Jong, T. Back, D. Fogel, H. de Garis. An overview of evolutionary computation. Springer,1993.
[23]J. H. Holland. Adapatation in nature and artificial systems. The University of Michigan Press,1975.
[24]J. R. Koza. Genetic programming:on the programming of computers by natural selection. The MIT Press,1992.
[25]L. J. Forgel. On the organization of intellect. University of California Ph.D. dissertation.1964.
[26]J. Kennedy. Handbook of nature-inspired and innovative computing "inbook" swarm intelligenced. Springer,2006.
[27]T. Hendtlass, M. Randall. A survey of ant colony and particle swarm meta-heuristics and their application to discrete optimization problems. Proceedings of the Inaugural Workshop on Artificial Life, Adelaide, South Australia,2001:15-25.
[28]唐贤伦.混沌粒子群优化算法理论及应用研究.重庆大学博十学位论文.2007.
[29]C. W. Reynolds. Flocks, herds and schools:a distributed behavioral model. Computer Graphics (ACM).1987,21(4):25-34.
[30]A. Colorni, M. Dorigo, V. Maniezzo. An investigation of some properties of an ant algorithm. Proceedings of the Parallel Problem Solving, Brussels, Belg.,1992:509-520.
[31]M. Dorigo, V. Maniezzo, A. Colorni. Ant system:optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics.1996,26(1):29-41.
[32]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.
[33]J. J. Hopfield, D. W. Tank. "Neural" computation of decisions in optimization problems. Biological Cybernetics.1985,52(3):141-152.
[34]N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, E. Teller. Equation of state calculations by fast computing machines. The Journal of Chemical Physics.1953,21(6):1087-1092.
[35]S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi. Optimization by simmulated annealing. Science.1983, 220(4598):671-680.
[36]F. Glover, M. Laguna. Tabu search. Kluwer Academic Pub.,1998.
[37]J. D. Farmer, N. H. Packard, A. S. Perelson. The immune system, adaptation, and machine learning. Physica.1986,2(3):187-204.
[38]R. G. Reynolds. An introduction to cultural algorithms. World Scientific Publishing Co.,1994.
[39]W. H. Durham. Coevolution:genes, culture, and human diversity. Stanford University Press,1991.
[40]L. M. Adleman. Molecular computation of solutions to combinatorial problems. Science.1994, 266(5187):1021-1024.
[41]支凌迎,殷志祥,黄晓慧,胡娟.DNA计算研究概述与分析.系统工程与电子技术.2009,31(6)：1462-1466.
[42]P. A. Benioff. Quantum mechanical hamiltonian models of discrete processes that erase their own histories:application to turing machines. International Journal of Theoretical Physics.1982,21(3): 177-201.
[43]R. P. Feynman. Simulating physics with computers. International Journal of Theoretical Physics. 1982,21(6):467-488.
[44]D. Deutsch. Quantum theory, the church-turing principle and the universal quantum computer. Proceedings of the Royal Society of London-Series A:Mathematical and Physical Sciences.1985, 400(1818):97-117.
[45]E. Bernstein, U. Vazirani. Quantum complexity theory. SIAM J. Comput,1997.
[46]R. Storn, K. Price. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization.1997,11(4):341-359.
[47]R. Storn. Differential evolution design of an IIR-filter. IEEE Congress on Evolutionary Computation, Nayoya University, Japan,1996:268-273.
[48]F.-S. Wang, J.-P. Chiou. Optimal control and optimal time location problems of differential-algebraic systems by differential evolution. Industrial & Engineering Chemistry Research.1997,36(12): 5348-5357.
[49]A.-Y. Qing. Electromagnetic inverse scattering of multiple two-dimensional perfectly conducting objects by the differential evolution strategy. IEEE Transactions on Antennas and Propagation.2003, 51(6):1251-1262.
[50]C. Lin, A.-Y. Qing, Q.-Y. Feng. Synthesis of unequally spaced antenna arrays by using differential evolution. IEEE Transactions on Antennas and Propagation.2010,58(8):2553-2561.
[51]林川.粒子群优化与差分进化算法研究及其应用.西南交通大学博士学位论文.2009.
[52]S. Xu, R. S. Yahya. Boundary conditions in particle swarm optimization revisited. IEEE Transactions on Antennas and Propagation.2007,55(3):760-765.
[53]Y.-H. Shi, R. Eberhart. A modified particle swarm optimizer. IEEE Congress on Evolutionary Computation, Anchorage, AK, USA,1998:69-73.
[54]M. Clerc, J. Kennedy. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation.2002,6(1): 58-73.
[55]R. C. Eberhart, Y.-H. Shi. Comparing inertia weights and constriction factors in particle swarm optimization. IEEE Congress on Evolutionary Computation, California, CA, USA,2000:84-88.
[56]A. Carlisle, G. Dozier. An off-the-shelf PSO. Proceedings of the Workshop on Particle Swarm Optimization, Indianapolis, Indiana, USA,2001:201-206.
[57]D. J. Watts. Collective dynamics of 'small-world' networks. Nature.1998,393(6864):440-442.
[58]D. J. Watts. Small worlds:the dynamics of networks between order and randomness. Princeton University Press,2003.
[59]J. Kennedy, R. Mendes. Neighborhood topologies in fully informed and best-of-neighborhood particle swarms. IEEE Transactions on Systems, Man, and Cybernetics.2006,36(4):515-519.
[60]J. Kennedy. Small worlds and mega-minds:effects of neighborhood topology on particle swarm performance. IEEE Congress on Evolutionary Computation, Washington, DC, USA,1999: 1931-1938.
[61]R. Mendes. Population topologies and their influence in particle swarm performance. University of Minho Ph.D. dissertation.2004.
[62]王雪飞.粒子群算法的动态拓朴结构研究.西南大学博士学位论文.2008.
[63]R. Poli, J. Kennedy, T. Blackwell. Particle swarm optimization. Swarm Intelligence.2007,1(1): 33-57.
[64]E. Ozcan, C. K. Mohan. Particle swarm optimization:surfing the waves. IEEE Congress on Evolutionary Computation, Washington, D.C., USA,1999:1944-1953.
[65]I. C. Trelea. The particle swarm optimization algorithm:convergence analysis and parameter selection. Information Processing Letters.2003,85(6):317-325.
[66]F. J. Solis, R. J. B. Wets. Minimization by random search techniques. Mathematics of Operations Research.1981,6(1):19-30.
[67]F. van den Bergh, A. P. Engelbrecht. A study of particle swarm optimization particle trajectories. Information Sciences.2006,176(8):937-971.
[68]V. Kadirkamanathan, K. Selvarajah, P. J. Fleming. Stability analysis of the particle dynamics in particle swarm optimizer. IEEE Transactions on Evolutionary Computation.2006,10(3):245-255.
[69]R. Poli, W. Langdon, M. Clerc, C. Stephens. Foundations of genetic algorithms. Springer Berlin Heidelberg,2007.
[70]潘峰,陈杰,甘明刚,蔡涛,涂序彦.粒子群优化算法模型分析.自动化学报.2006,32(3)：368-377.
[71]李宁,孙德宝,邹彤,秦元庆,尉宇.基于差分方程的PSO算法粒子运动轨迹分析.计算机学报.2006,29(11)：2052-2060.
[72]H. Liu, A. Abraham, M. Clerc. Chaotic dynamic characteristics in swarm intelligence. Applied Soft Computing.2007,7(3):1019-1026.
[73]Y.-H. Shi, R. C. Eberhart. Fuzzy adaptive particle swarm optimization. IEEE Congress on Evolutionary Computation, Seoul, South Korea,2001:101-106.
[74]A. Ratnaweera, S. K. Halgamuge, H. C. Watson. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation.2004,8(3):240-255.
[75]林川,冯全源.一种新的自适应粒子群优化算法.计算机工程.2008,34(7)：181-183.
[76]A.-K. Buthainah, K. M. Chilukuri. Multi-phase generalization of the particle swarm optimization algorithm. IEEE Congress on Evolutionary Computation, Honolulu, HI, USA,2002:489-494.
[77]F. van den Bergh, A. P. Engelbrecht. A new locally convergent particle swarm optimiser. IEEE Conference on Systems, Man and Cybernetics, Hammamet, Tunisia,2002:184-190.
[78]T. Peram, K. Veeramachaneni, C. K. Mohan. Fitness-distance-ratio based particle swarm optimization. Proceedings of the IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA,2003:174-181.
[79]J.-J. Liang, A.-K. Qin, P. N. Suganthan, S. Baskar. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation. 2006,10(3):281-295.
[80]Y. Chunming, D. Simon. A new particle swarm optimization technique.18th International Conference on Systems Engineering, Las Vegas, NV, USA,2005:164-169.
[81]A. I. Selvakumar, K. Thanushkodi. A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Transactions on Power Systems.2007,22(1):42-51.
[82]S. He, Q.-H. Wu, J.-Y. Wen, J. R. Saunders, R. C. Paton. A particle swarm optimizer with passive congregation. Biosystems.2004,78(3):135-147.
[83]W. Jian, Y. Xue, J. Huang. Combined algorithm for time-varying system based on improved particle swarm optimization and EWRLS algorithm. Proceedings of the IEEE International Conference on Industrial Technology, Hammamet, Tunisia,2004:1474-1477
[84]孟红记,郑鹏,梅国晖,谢植.基于混沌序列的粒子群优化算法.控制与决策.2006,21(3)：263-266.
[85]J. Kennedy. Stereotyping:improving particle swarm performance with cluster analysis. IEEE Congress on Evolutionary Computation, La Jolla, CA, USA,2000:1507-1512.
[86]R. Mendes, J. Kennedy, J. Neves. The fully informed particle swarm:simpler, maybe better. IEEE Transactions on Evolutionary Computation.2004,8(3):204-210.
[87]F. van den Bergh, A. P. Engelbrecht. Training product unit networks using cooperative particle swarm optimisers. International Joint Conference on Neural Networks, Vienna, Austria,2001:126-131.
[88]P. N. Suganthan. Particle swarm optimiser with neighbourhood operator. IEEE Congress on Evolutionary Computation, Washington, DC, USA,1999:1958-1962.
[89]B. Niu, Y. Zhu, X. He, H. Wu. MCPSO:a multi-swarm cooperative particle swarm optimizer. Applied Mathematics and Computation.2007,185(2):1050-1062.
[90]E. A. Mohammed, K. Mohamed. Cooperative particle swarm optimizers:a powerful and promising approach. Stigmergic Optimization.2006,31:239-259.
[91]S. Selleri, M. Mussetta, P. Pirinoli, R. E. Zich, L. Matekovits. Some insight over new variations of the particle swarm optimization method. Antennas and Wireless Propagation Letters.2006,5(1): 235-238.
[92]X.-H. Shi, Y.-H. Lu, C.-G. Zhou, H.-P. Lee, W.-Z. Lin, Y.-C. Liang. Hybrid evolutionary algorithms based on PSO and GA. IEEE Congress on Evolutionary Computation, Canberra, Australia,2003: 2393-2399.
[93]P. J. Angeline. Using selection to improve particle swarm optimization. IEEE World Congress on Computational Intelligence Evolutionary Computation Proceedings, Anchorage, AK, USA,1998: 84-89.
[94]W. H. Duminy, A. P. Engelbrecht. Tournament particle swarm optimization. IEEE Symposium on Computational Intelligence and Games, Honolulu, HI, USA,2007:146-153.
[95]B. Yang, Y. Chen, Z. Zhao. A hybrid evolutionary algorithm by combination of PSO and GA for unconstrained and constrained optimization problems. IEEE Conference on Control and Automation, Wuhan Univ., Wuhan,2007:166-170.
[96]郑小霞,钱锋.一种改进的微粒群优化算法.计算机工程.2006,32(15)：25-27.
[97]T. Hendtlass. Engineering of intelligent systems lecture notes in computer science. Springer,2001.
[98]A. Kaveh, S. Talatahari. Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Computers & Structures.2009,87(5):267-283.
[99]窦全胜,周春光,马铭.粒子群优化的两种改进策略.计算机研究与发展.2005,42(5)：879-904.
[100]N. Higashi, H. Iba. Particle swarm optimization with gaussian mutation. Swarm Intelligence Symposium, Indianapolis, Indiana, USA,2003:72-79.
[101]L. Morten, R. Thomas Kiel, K. Thiemo. Hybrid particle swarm optimiser with breeding and subpopulations. Proceedings of the Third Genetic Algorithm, San Francisco, California, USA,2001: 469-476.
[102]B. Liu, L. Wang, Y.-H. Jin, F. Tang, D.-X. Huang. Improved particle swarm optimization combined with chaos. Chaos, Solitons and Fractals.2005,25(5):1261-1271.
[103]费春国,韩正之.一种改进的混沌优化算法.控制理论与应用.2006,23(3)：471-474.
[104]J. Kennedy, R. C. Eberhart. A discrete binary version of the particle swarm algorithm. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Orlando, FL, USA,1997: 4104-4108.
[105]崔志华,曾建潮.基于控制理论的微粒群算法分析与改进.小型微型计算机系统.2006,27(5)：1333-1338.
[106]A. Acan, A. Unveren, M. Bodur. The latest vs. averaged recent experience:which better guides a PSO algorithm? IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada,2006:414-419.
[107]T. Ray, K. Liew. A swarm metaphor for multiobjective design optimization. Engineering Optimization. 2002,34(2):141-153.
[108]K. E. Parsopoulos, M. N. Vrahatis. Particle swarm optimization method for constrained optimization problems. Proceedings of the Euro-International Symposium on Computational Intelligence, Shfeim, Israel,2002:214-220.
[109]M. Abido. Optimal design of power-system stabilizers using particle swarm optimization. IEEE Transactions on Energy Conversion.2002,17(3):406-413.
[110]H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, Y. Nakanishi. A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Transactions on Power Systems.2000,15(4):1232-1239.
[111]J.-R. Zhang, J. Zhang, T. M. Lok, M. R. Lyu. A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation.2007, 185(2):1026-1037.
[112]X. Zhang, L. Yu, Y. Zheng. Two-stage adaptive PMD compensation in a 10 Gbit/s optical communication system using particle swarm optimization algorithm. Optics Communications.2004, 231(6):233-242.
[113]H.-W. Liu, J. Li. A particle swarm optimization-based multiuser detection for receive-diversity-aided STBC systems. Signal Processing Letters.2008,15:29-32.
[114]T. K. Rasmussen, T. Krink. Improved hidden markov model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid. Biosystems.2003,72(2):5-17.
[115]X. Xiao, E. R. Dow, R. Eberhart, B. Z. Miled, R. J. Oppelt. A hybrid self-organizing maps and particle swarm optimization approach. Concurrency and Computation:Practice and Experience.2004,16(9): 895-915.
[116]W.-T. Li, X.-W. Shi, Y.-Q. Hei, S.-F. Liu, J. Zhu. A hybrid optimization algorithm and its application for conformal array pattern synthesis. IEEE Transactions on Antennas and Propagation.2010,58(10): 3401-3406.
[117]S. K. Goudos, V. Moysiadou, T. Samaras, K. Siakavara, J. N. Sahalos. Application of a comprehensive learning particle swarm optimizer to unequally spaced linear array synthesis with sidelobe level suppression and null control. Antennas and Wireless Propagation Letters.2010,9: 125-129.
[118]T. H. Ismail, Z. M. Hamici. Array pattern synthesis using digital phase control by quantized particle swarm optimization. IEEE Transactions on Antennas and Propagation.2010,58(6):2142-2145.
[119]金荣洪,袁智皓,耿军平,范瑜,李佳靖.基于改进粒子群算法的天线方向图综合技术.电波科学学报.2006,21(6)：873-878.
[120]Z. L. Gaing. A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Transactions on Energy Conversion.2004,19(2):384-391.
[121]A. E. M. Zavala, E. R. V. Diharce, A. H. Aguirre. Particle evolutionary swarm for design reliability optimization. Lecture Notes in Computer Science.2005,3410:856-869.
[122]A. Elwer, S. Wahsh, M. Khalil, A. Nur-Eldeen. Intelligent fuzzy controller using particle swarm optimization for control of permanent magnet synchronous motor for electric vehicle. The 29th Annual Conference of the IEEE Industrial Electronics Society, Roanoke, VA, USA,2003: 1762-1766
[123]V. G. Gudise, G. K. Venayagamoorthy. FPGA placement and routing using particle swarm optimization. IEEE Computer Society Annual Symposium on VLSI:Emerging Trends in VLSI Systems Design, Lafayette, LA, USA,2004:307-308.
[124]张宝.粒子群算法及其在卫星舱布局中的应用研究.大连理工大学博士学位论文.2006.
[125]W. Zhong, J. Zhang, W. Chen. A novel discrete particle swarm optimization to solve traveling salesman problem. IEEE Congress on Evolutionary Computation, Singapore,2007:3283-3287.
[126]C. A. C. Coello, G. T. Pulido, M. S. Lechuga. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation.2004,8(3):256-279.
[127]M. G. H. Omran, A. Salman, A. P. Engelbrecht. Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Analysis & Applications.2006,8(4):332-344.
[128]K. E. Parsopoulos, E. I. Papageorgiou, P. P. Groumpos, M. N. Vrahatis. Evolutionary computation techniques for optimizing fuzzy cognitive maps in radiation therapy systems. Genetic and Evolutionary Computation, Seattle, Washington, USA,2004:402-413.
[129]S. Das, A. Abraham, A. Konar. Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. Pattern Recognition Letters.2008,29(5):688-699.
[130]C. O. Ourique, E. C. Biscaia. The use of particle swarm optimization for dynamical analysis in chemical processes. Computers & Chemical Engineering.2002,26(12):1783-1793.
[131]G. K. Venayagamoorthy, S. Doctor. Navigation of mobile sensors using PSO and embedded P SO in a fuzzy logic controller.39th IAS Annual Meeting Industry Applications Conference, Rolla, MO, USA, 2004:1200-1206
[132]T. Ray, K. M. Liew. A swarm with an effective information sharing mechanism for unconstrained and constrained single objective optimisation problems. IEEE Congress on Evolutionary Computation, Seoul, South Korea,2001:75-80
[133]X.-H. Hu, R. Eberhart. Solving constrained nonlinear optimization problems with particle swarm optimization.6th World Multiconference on Systemics, Cybernetics and Informatics, Orlando, Florida, USA,2002:203-206.
[134]Q. He, L. Wang. A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Applied Mathematics and Computation.2007,186(2):1407-1422.
[135]J. Kennedy, W. M. Spears. Matching algorithms to problems:an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. IEEE World Congress on Computational Intelligence Evolutionary Computation Proceedings, Anchorage, AK, USA,1998: 78-83.
[136]X.-H. Hu, R. C. Eberhart, Y.-H. Shi. Swarm intelligence for permutation optimization:a case study of n-queens problem. Proceedings of the IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA,2003:243-246.
[137]M. Clerc. New optimization techniques in engineering. Springer-Verlag,2004.
[138]G. Pampara, N. Franken, A. Engelbrecht. Combining particle swarm optimisation with angle modulation to solve binary problems. IEEE Congress on Evolutionary Computation, Edinburgh, Scotland,2005:89-96.
[139]K. E. Parsopoulos, M. N. Vrahatis. Particle swarm optimization method in multiobjective problems. ACM symposium, Madrid, Spain,2002:603-607.
[140]G. T. Pulido, C. A. Coello. Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer. Genetic and Evolutionary Computation, Seattle, Washington, USA,2004:225-237.
[141]X.-D. Li. A non-dominated sorting particle swarm optimizer for multiobjective optimization. Genetic and Evolutionary Computation, Chicago, II, USA,2003:198-198.
[142]王维博,林川,郑永康.粒子群算法中参数的实验与分析.西华大学学报(自然科学版).2008,27(1)：76-80.
[143]Y.-H. Shi, R. C. Eberhart. Parameter selection in particle swarm optimization. Proceedings of the 7th International Conference on Evolutionary Programming, San Diego, CA, USA,1998:591-600.
[144]Y.-H. Shi, R. C. Eberhart. Empirical study of particle swarm optimization. IEEE Congress on Evolutionary Computation, Washington, D.C., USA,1999:1945-1950.
[145]J. Robinson, R.-S. Yahya. Particle swarm optimization in electromagnetics. IEEE Transactions on Antennas and Propagation.2004,52(2):397-407.
[146]T. Huang, A. S. Mohan. A hybrid boundary condition for robust particle swarm optimization. Antennas and Wireless Propagation Letters.2005,4:112-117.
[147]X. Yao, Y. Liu, G. Lin. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation.1999,3(2):82-102.
[148]王俊伟,汪定伟.粒子群算法中惯性权重的实验与分析.系统工程学报.2005,20(2)：194-198.
[149]L.-P. Zhang, H.-J. Yu, S.-X. Hu. Optimal choice of parameters for particle swarm optimization. Journal of Zhejiang University-Science A.2005,6(6):528-534.
[150]W.-B. Wang, Q.-Y. Feng, Y.-K. Zheng. A novel particle swarm optimization algorithm with stochastic focusing search for real-parameter optimization. 11th IEEE Singapore International Conference on Communication Systems, Guangzhou, China,2008:583-587.
[151]W.-B. Wang, Q.-Y. Feng. A hierarchical particle swarm optimization algorithm combined with chaotic search. International Conference on Computer and Communication Technologies in Agriculture Engineering, Chengdu, China,2010:435-438.
[152]王维博,冯全源.基于分层多子群的混沌粒子群优化算法.控制与决策.2010,25(11)：1663-1668.
[153]M. H. Wright. Direct search methods:once scorned, now respectable. Proceedings of the Dundee Biennial Conference in Numerical analysis, Harlow, UK,1995:191-208.
[154]M. W. Trosset. I know it when I see it:toward a definition of direct search methods. Citeseer,1997.
[155]K. Penev, G. Littlefair. Free search-a comparative analysis. Information Sciences.2005,172(1): 173-193.
[156]E. Burke, G. Kendall, J. Newall, E. Hart, P. Ross, S. Schulenburg. Hyper-heuristics:an emerging direction in modern search technology. Springer,2003.
[157]S. J. Russell, P. Norvig, J. F. Candy, J. M. Malik, D. D. Edwards. Artificial intelligence:a modern approach. Prentice Hall,2010.
[158]高波,田东,胡亮,傅泽田.Free Search—一种新的进化算法.仪器仪表学报.2006,27(Z1)：819-821.
[159]李奇,陈维荣,段涛,刘述奎,刘元勋.基于随机聚焦粒子群算法的多机系统PSS参数优化.电力系统保护与控制.2009,37(13)：13-18.
[160]郑永康,陈维荣,戴朝华,王维博.随机聚焦搜索优化算法.控制理论与应用.2009,26(8)：915-917.
[161]C.-F. Juang, C.-M. Hsiao, C.-H. Hsu. Hierarchical cluster-based multispecies particle-swarm optimization for fuzzy-system optimization. IEEE Transactions on Fuzzy Systems.2010,18(1):14-26.
[162]S. Janson, M. Middendorf. A hierarchical particle swarm optimizer and its adaptive variant. IEEE Transactions on Systems, Man, and Cybernetics.2005,35(6):1272-1282.
[163]S. Janson, M. Middendorf. A hierarchical particle swarm optimizer for noisy and dynamic environments. Genetic Programming and Evolvable Machines.2006,7(4):329-354.
[164]C.-C. Chen. Two-layer particle swarm optimization for unconstrained optimization problems. Applied Soft Computing.2011,11(1):295-304.
[165]C.-F. Juang. A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Transactions on Systems, Man, and Cybernetics.2004,34(2):997-1006.
[166]C. Lin, Q.-Y. Feng. A hierarchical subpopulation particle swarm optimization algorithm. Intelligent Systems and Knowledge Engineering, Chengdu, Sichuan, China,2007:1487-1491.
[167]宋莹.基于混沌和粒子群优化的非线性建模与控制研究.南开大学博十学位论文.2007.
[168]T.-Y. Li, J. A. Yorke. Period three implies chaos. American Mathematical Monthly.1975,82(10): 985-992.
[169]R. M. May. Simple mathematical models with very complicated dynamics. Nature.1976,261(5560): 459-467.
[170]M. J. Feigenbaum. Quantitative universality for a class of nonlinear transformations. Journal of Statistical Physics.1978,19(1):25-52.
[171]李兵,蒋慰孙.混沌优化方法及其应用.控制理论与应用.1997,14(4)：613-615.
[172]B. Li, W.-S. Jiang. Optimizing complex functions by chaos search. Cybernetics and Systems.1998, 29(4):409-419.
[173]张彤,王宏伟,王子才.变尺度混沌优化方法及其应用.控制与决策.1999,14(3)：285-288.
[174]T. Kwok, K. A. Smith. A unified framework for chaotic neural-network approaches to combinatorial optimization. IEEE Transactions on Neural Networks.1999,10(4):978-981.
[175]T. Tanaka, E. Hiura. Computational abilities of a chaotic neural network. Physics Letters A.2003, 315(3):225-230.
[176]C.-S. Zhou, T.-L. Chen, W.-Q. Huang. Chaotic neural network with nonlinear self-feedback and its application in optimization. Neurocomputing.1997,14(3):209-222.
[177]尤勇,王孙安,盛万兴.新型混沌优化方法的研究及应用.西安交通大学学报.2003,37(1)：69-72.
[178]单梁,强浩,李军,王执铨.基于Tent映射的混沌优化算法.控制与决策.2005,20(2)：179-182.
[179]Q. Wu. A self-adaptive embedded chaotic particle swarm optimization for parameters selection of Wv-S VM. Expert Systems with Applications.2011,38(1):184-192.
[180]Q. Tao, H.-Y. Chang, Y. Yi, C.-Q. Gu, W.-J. Li. A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow. Computers and Operations Research.2011,38(5):824-836.
[181]L.-Y. Chuang, C.-H. Yang, J.-C. Li. Chaotic maps based on binary particle swarm optimization for feature selection. Applied Soft Computing.2011,11(1):239-248.
[182]H.-B. Zhao, S.-D. Yin. A CPSO-SVM model for ultimate bearing capacity determination. Marine Georesources and Geotechnology.2010,28(1):64-75.
[183]Y. Wang, J.-Z. Zhou, H. Qin, Y. Lu. Improved chaotic particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects. Energy Conversion and Management. 2010,51(12):2893-2900.
[184]J. B. Park, Y. W. Jeong, J. R. Shin, K. Y. Lee. An improved particle swarm optimization for nonconvex economic dispatch problems. IEEE Transactions on Power Systems.2010,25(1):156-166.
[185]雷德明.利用混沌搜索全局最优解的一种混合遗传算法.系统工程与电子技术.1999,21(12)：81-82.
[186]X.-F. Zou, M. Wang, A. Zhou, B. Mckay. Evolutionary optimization based on chaotic sequence in dynamic environments. IEEE Conference on Networking, Sensing and Control, Taipei, Taiwan,2004: 1364-1369.
[187]李亚东,李少远.一种新的遗传混沌优化组合方法.控制理论与应用.2002,19(1)：143-145.
[188]L. Wang, K. Smith. On chaotic simulated annealing. IEEE Transactions on Neural Networks 1998, 9(4):716-718.
[189]徐宁,州尚波,张红民,虞厥邦.一种混合混沌优化方法及其应用.系统工程与电子技术.2003,25(2)：226-227+244.
[190]张志新,张明廉.基于并行混沌和单纯形法的混合全局优化算法.系统仿真学报.2004,16(1)：35-37.
[191]林川,冯全源.基于微粒群本质特征的混沌微粒群优化算法.西南交通大学学报.2007,42(6)：665-669.
[192]X. Tao, X.-F. Liao, K.-W. Wong. An improved particle swarm optimization algorithm combined with piecewise linear chaotic map. Applied Mathematics and Computation.2007,190(2):1637-1645.
[193]J. B. Park, Y. W. Jeong, W. N. Lee, J. R. Shin. An improved particle swarm optimization for economic dispatch problems with non-smooth cost functions. Power Engineering Society General Meeting, Montreal, Quebec, Canada,2006:121-126.
[194]J. Kennedy. In search of the essential particle swarm. IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada,2006:1694-1701.
[195]丁鑫鑫,唐常杰,曾涛,张培颂,徐开阔,刘齐宏.基于最佳粒子共享和分层搜索的并行粒子群优化算法.四川大学学报(自然科学版).2008,45(4)：799-803.
[196]范九伦,张雪锋.分段Logistic混沌映射及其性能分析.电子学报.2009,37(4)：720-725.
[197]C.-W. Jiang, E. Bompard. A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation. Mathematics and Computers in Simulation.2005,68(1):57-65.
[198]王巍,彭力.嵌入隔离小生境技术的混沌粒子群算法.系统工程与电子技术.2008,30(6)：1151-1154.
[199]吕振肃,侯志荣.自适应变异的粒子群优化算法.电子学报.2004,32(3)：416-420.
[200]陈如清,俞金寿.混沌粒子群混合优化算法的研究与应用.系统仿真学报.2008,20(3)：685-688.
[201]A. Chatterjee, P. Siarry. Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers and Operations Research.2006,33(3):859-871.
[202]王维博,冯全源.基于改进粒子群算法的工程项目综合优化.西南交通大学学报.2011,46(1)：76-83.
[203]成虎.工程项目管理.中国建筑共业出版社,2009.
[204]符志民.项目管理理论与实践.中国宇航出版社,2002.
[205]范黎波.项目管理.对外贸易大学出版社,2005.
[206]杨坤.项目时间管理.南开大学出版社,2006.
[207]朱宏亮.项目进度管理.清华大学出版社,2002.
[208]周艳.工程项目工期成本质量综合优化研究.西南交通大学硕士学位论文.2008.
[209]R. McKim, T. Hegazy, M. Attalla. Project performance control in reconstruction projects. Journal of Construction Engineering and Management.2000,126(2):137-141.
[210]P. Rwelamila, K. Hall. Total systems intervention:an integrated approach to time, cost and quality management. Construction Management and Economics.1995,13(3):235-241.
[211]S. W. Hsiao. Fuzzy logic based decision model for product design. International Journal of Industrial Ergonomics.1998,21(2):103-116.
[212]熊鹰,匡亚萍.基于蚁群算法的施工项目工期—成本优化.系统工程理论与实践.2007,27(3)：105-111.
[213]王健,刘尔烈,骆刚.工程项目管理中工期—成本—质量综合均衡优化.系统工程学报.2004,19(2)：148-153.
[214]杨耀红,汪应洛,王能民.工程项目工期成本质量模糊均衡优化研究.系统工程理论与实践.2006,26(7)：112-117.
[215]高兴夫,胡程顺,钟登华.工程项目管理的工期一费用—质量综合优化研究.系统工程理论与实践.2007,27(10)：112-117.
[216]E.-R. Khaled, K. Amr. Time-cost-quality trade-off analysis for highway construction. Journal of Construction Engineering and Management.2005,131(4):477-486.
[217]骆刚,刘尔烈,王健.遗传算法在网络计划资源优化中的应用.天津大学学报.2004,37(2)：179-183.
[218]刘晓峰,陈通,张连营.基于微粒群算法的工程项目质量、费用和工期综合优化.土木工程学报.2006,39(10)：122-126.
[219]陈志勇,杜志达,周华.基于微粒群算法的工程项目资源均衡优化.土木工程学报.2007,40(2)：93-96.
[220]R. Storn, K. Price. Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science.1995,7(12):1-12.
[221]刘鸣,袁超伟,贾宁,黄韬.智能天线的技术与应用.机械工业出版社,2007.
[222]C. K. Ng, N. K. Noordin, S. Khatun, B. M. Ali, S. S. Jamuar, M. Ismail. Directional diversity of smart antenna in LAS CDMA systems. Wireless Personal Communications.2008,46(3):305-316.
[223]尚飞,蔡亚星,张颖,高本庆.阵列天线的双种群遗传算法综合.电波科学学报.2007,1l(2)：224-228.
[224]C. B. Dietrich, W. L. Stutzman, B. K. Kim, K. Dietze. Smart antennas in wireless communications: base-station diversity and handset beamforming. Antennas and Propagation Magazine.2000,42(5): 142-151.
[225]Z. Zaharis. Radiation pattern shaping of a mobile base station antenna array using a particle swarm optimization based technique. Electrical Engineering.2008,90(4):301-311.
[226]杨林.阵列天线综合方法研究.哈尔滨工程大学博士学位论文.2006.
[227]叶剑锋.基于免疫优化算法的阵列天线综合的研究.哈尔滨工程大学博十学位论文.2009.
[228]王维博,冯全源.粒子群优化算法在天线方向图综合中的应用.电子科技大学学报.2011,40(2)：237-241.
[229]王维博,冯全源.粒子群算法在阵列天线方向图综合中的应用.西安电子科技大学学报.2011,38(3)：175-180.
[230]W.-B. Wang, Q.-Y. Feng, D. Liu. Application of chaotic particle swarm optimization algorithm to pattern synthesis of antenna arrays. Progress in Electromagnetics Research.2011,115:173-189.
[231]W.-B. Wang, Q.-Y. Feng, D. Liu. Synthesis of thinned linear and planar antenna arrays using binary PSO algorithm. Progress in Electromagnetics Research.2012,127:371-387.
[232]金荣洪,耿军平,范瑜.无线通信中的智能天线.北京邮电大学出版社,2006.
[233]吕善伟.天线阵综合.北京航空学院出版社,1988.
[234]N.-B. Jin, R.-S. Yahya. Advances in particle swarm optimization for antenna designs:real-number, binary, single-objective and multiobjective implementations. IEEE Transactions on Antennas and Propagation.2007,55(3):556-567.
[235]D. G Kurup, M. Himdi, A. Rydberg. Synthesis of uniform amplitude unequally spaced antenna arrays using the differential evolution algorithm. IEEE Transactions on Antennas and Propagation.2003, 51(9):2210-2217.
[236]C. Dolph. A current distribution for broadside arrays which optimizes the relationship between beam width and side-lobe level. Proceedings of the IRE.1946,34(6):335-348.
[237]T. Taylor. Design of circular apertures for narrow beamwidth and low sidelobes. IRE Transactions on Antennas and Propagation.1960,8(1):17-22.
[238]B. P. Kumar, G. Branner. Design of unequally spaced arrays for performance improvement. IEEE Transactions on Antennas and Propagation.1999,47(3):511-523.
[239]B. Kumar, G. Branner. Generalized analytical technique for the synthesis of unequally spaced arrays with linear, planar, cylindrical or spherical geometry. IEEE Transactions on Antennas and Propagation.2005,53(2):621-634.
[240]J. M. Johnson, Y. Rahmat-Samii. Genetic algorithm optimization and its application to antenna design. Antennas and Propagation Society International Symposium, Seattle, WA, USA,1994:326-329
[241]R. L. Haupt. Thinned arrays using genetic algorithms. IEEE Transactions on Antennas and Propagation.1994,42(7):993-999.
[242]K.-K. Yan, Y. Lu. Sidelobe reduction in array-pattern synthesis using genetic algorithm. IEEE Transactions on Antennas and Propagation.1997,45(7):1117-1122.
[243]A. Tennant, M. Dawoud, A. Anderson. Array pattern nulling by element position perturbations using a genetic algorithm. Electronics Letters.1994,30(3):174-176.
[244]叶剑锋,王玉峰,庞伟正.多映射混沌免疫算法及其在天线阵方向图综合中的应用.哈尔滨工程大学学报.2007,28(11)：1273-1277.
[245]姚昆,杨万麟.最佳稀布直线阵列的分区动态规划法.电子学报.1994,22(2)：87-90.
[246]V. Murino, A. Trucco, C. S. Regazzoni. Synthesis of unequally spaced arrays by simulated annealing. IEEE Transactions on Signal Processing.1996,44(1):119-122.
[247]范瑜,金荣洪,耿军平,刘波.基于差分进化算法和遗传算法的混合优化算法及其在阵列天线方向图综合中的应用.电子学报.2004,32(12)：1997-2000.
[248]Y.-K. Chen, S.-W. Yang, Z.-P. Nie. The application of a modified differential evolution strategy to some array pattern synthesis problems. IEEE Transactions on Antennas and Propagation.2008,56(7): 1919-1927.
[249]高波,金荣洪,耿军平.基于一种新的粒子群算法的天线方向图综合技术.电子与信息学报.2007,29(12)：3015-3019.
[250]焦永昌,杨科,陈胜兵,张福顺.粒子群优化算法用于阵列天线方向图综合设计.电波科学学报.2006,21(1)：16-20.
[251]K. T. Chaturvedi, M. Pandit, L. Srivastava. Self-organizing hierarchical particle swarm optimization for nonconvex economic dispatch. IEEE Transactions on Power Systems.2008,8(3):1079-1087.
[252]J. Kennedy, R. Mendes. Population structure and particle swarm performance. IEEE Congress on Evolutionary Computation, Honolulu, HI, USA,2002:1671-1676.
[253]范瑜,金荣洪.基于一种新的遗传算法的天线方向图综合技术.电波科学学报.2004,19(2)：182-186.
[254]刘道华,原思聪,兰洋,马新建.混沌映射的粒子群优化方法.西安电子科技大学学报.2010,37(4)：764-769.
[255]张顶学,廖锐全.一种基于种群速度的自适应粒子群算法.控制与决策.2009,24(8)：1257-1260.
[256]H. Modares, A. Alfi, M.-M. Fateh. Parameter identification of chaotic dynamic systems through an improved particle swarm optimization. Expert Systems with Applications.2010,37(5):3714-3720.
[257]Y.-Y. He, J.-Z. Zhou, X.-Q. Xiang, H. Chen, H. Qin. Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling. Chaos, Solitons and Fractals.2009,42(5):3169-3176.
[258]J. R. Perez, J. Basterrechea. Hybrid particle swarm-based algorithms and their application to linear array synthesis. Progress in Electromagnetics Research.2009,90:63-74.
[259]刘东,冯全源.基于停滞检测粒了群算法的阵列天线方向图综合.电波科学学报.2009,24(4)： 697-701.
[260]M. M. Khodier, C. G. Christodoulou. Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization. IEEE Transactions on Antennas and Propagation. 2005,53(8):2674-2679.
[261]J. A. Ferreira, F. Ares. Pattern synthesis of conformal arrays by the simulated annealing technique. Electronics Letters.1997,33(14):1187-1189.
[262]B. Fuchs. Shaped beam synthesis of arbitrary arrays via linear programming. Antennas and Wireless Propagation Letters.2010,9:481-484.
[263]H.-J. Zhou, B.-H. Sun, J.-F. Li, Q.-Z. Liu. Efficient optimization and realization of a shaped-beam planar array for very large array application. Progress in Electromagnetics Research.2009,89:1-10.
[264]陈客松.稀布天线阵列的优化布阵技术研究.电子科技大学博十学位论文.2006.
[265]Z. D. Zaharis, T. V. Yioultsis. A novel adaptive beamforming technique applied on linear antenna arrays using adaptive mutated boolean PSO. Progress in Electromagnetics Research.2011,117: 165-179.
[266]G. Oliveri, L. Manica, A. Massa. ADS-based guidelines for thinned planar arrays. IEEE Transactions on Antennas and Propagation.2010,58(6):1935-1948.
[267]S. K. Goudos, K. Siakavara, T. Samaras, E. E. Vafiadis, J. N. Sahalos. Sparse linear array synthesis with multiple constraints using differential evolution with strategy adaptation. Antennas and Wireless Propagation Letters.2011,10:670-673.
[268]K.-S. Chen, Z.-S. He, C.-L. Han. A modified real GA for the sparse linear array synthesis with multiple constraints. IEEE Transactions on Antennas and Propagation.2010,58(11):3770-3770.
[269]L. Zhang, Y.-C. Jiao, Z.-B. Weng, F.-S. Zhang. Design of planar thinned arrays using a boolean differential evolution algorithm. IETMicrowaves, Antennas & Propagation.2010,4(12):2172-2178.
[270]G. K. Mahanti, N. Pathak, P. Mahanti. Synthesis of thinned linear antenna arrays with fixed sidelobe level using real-coded genetic algorithm. Progress in Electromagnetics Research.2007,75:319-328.
[271]O. Quevedo-Teruel, E. Rajo-Iglesias. Ant colony optimization in thinned array synthesis with minimum sidelobe level. Antennas and Wireless Propagation Letters.2006,5(1):349-352.
[272]K.-S. Chen, X.-H. Yun, Z.-S. He, C.-L. Han. Synthesis of sparse planar arrays using modified real genetic algorithm. IEEE Transactions on Antennas and Propagation.2007,55(4):1067-1073.