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微粒群优化算法参数性能实验分析 被引量：3

Experimental Analysis of Parameters of Particle Swarm Optimization Algorithm
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摘要 通过测试实验来分析微粒群优化算法参数的性能,针对算法参数对算法性能的影响进行实验分析,最后给出相关结论并就如何选择合适的算法参数提出建议。 The optimization performance of parameters of particle swarm optimization algorithm （PSO） is analysed through a lot of experiments in this paper. The relationship between algorithm parameters and optimization performance of PSO is studied based on experimental results. Some conclusions on the optimization performance of parameters of PSO are presented and the question of how to choose appropriate values for parameters of PSO is studied.
作者 陈国初 徐余法 李承阳
机构地区 上海电机学院电气学院 乐凯集团第二胶片厂
出处 《上海电机学院学报》 2007年第2期86-92,共7页 Journal of Shanghai Dianji University
基金 上海市教委科研项目(05VZ01 06VZ002)
关键词 微粒群优化算法 算法参数 性能 实验分析 particle swarm optimization algorithm parameters optimization performance experimental analysis
分类号 TP273 [自动化与计算机技术—检测技术与自动化装置]
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参考文献6

  • 1[1]Kennedy J,Eberhart R C.Particle Swarm Optimization[C].Proc.IEEE Int.Conference on Neural Networks.Piscataway,NJ:IEEE Service Center,1995:1942-1948.
  • 2[2]Eberhart R C,Kennedy J.A New Optimizer Using Particle Swarm Theory[C].Proc.the Sixth Int.Symposium on Micro Machine and Human Science.Nagoya,Japan:IEEE Service Center,1995:39-43.
  • 3[3]Eberhart R C,Shi Y.Particle Swarm Optimization:Developments,Applications and Resources[A]Proc.2001 Congress on Evolutionary Computation[C].Seoul,South Korea:IEEE Service Center,2001:81-86.
  • 4陈国初,俞金寿.增强型微粒群优化算法及其在软测量中的应用[J].控制与决策,2005,20(4):377-381. 被引量：31
  • 5[5]Bergh F van den,Engelbrecht A P.A Study of Particle Swarm Optimization Particle Trajectories[J].Information Sciences.2006,176:937-971.
  • 6张丽平,俞欢军,陈德钊,胡上序.粒子群优化算法的分析与改进[J].信息与控制,2004,33(5):513-517. 被引量：86

二级参考文献17

  • 1[1]Kennedy J, EberhartRC. Particle swarm optimization [A]. Proceedings of IEEE International Conference on Neural Networks [C]. Piscataway, NJ: IEEE Press, 1995.1942 ～ 1948.
  • 2[2]Eberhart R C, Kennedy J. A new optimizer using particle swarm theory [A]. Proceedings of the Sixth International Symposium on Micro Machine and Human Science [ C]. Nagoya, Japan: IEEE Press, 1995. 39～43.
  • 3[3]Eberhart R C, Simpson P K, Dobbins R W. Computational Intelligence PC Tools [M]. Boston, MA: Academic Press Professional,1996.
  • 4[4]Shi Y, Eberhart R C. A modified particle swarm optimizer [A].Proceedings of the IEEE Congress on Evolutionary Computation [C]. Piscataway, NJ: IEEE Press, 1998.303～308.
  • 5[5]Shi Y, Eberhart R C. Empirical study of particle swarm optimization [A]. Proceedings of the IEEE Congress on Evolutionary Computation [C]. Piscataway, NJ: IEEE Press, 1999.1945 ～ 1950.
  • 6[6]Shi Y, Eberhart R C. Fuzzy adaptive particle swarm optimization [A]. Proceedings of the IEEE Congress on Evolutionary Computation [C]. Seoul, Korea: IEEE Press, 2001. 101 ～106.
  • 7[7]Clerc M, Kennedy J. The particle swarm - explosion, stability,and convergence in a multidimensional complex space [ J ]. IEEE Transactions on Evolutionary Computation, 2002,6( 1 ): 58 ～73.
  • 8[8]Eberhart R C, Shi Y. Comparing inertia weight and constriction factors in particle swarm optimization [ A ]. Proceedings of the IEEE Congress on Evolutionary Computation [ C ]. San Diego,CA: IEEE Press, 2000.84 ～ 88.
  • 9[9]Miranda V, Fonseca N. EPSO-best-of-two-worlds meta-heuristic applied to power system problems [ A ]. Proceedings of the IEEE Congress on Evolutionary Computation [ C ]. Honolulu, Hawaii,USA: IEEE Press, 2002. 1080 ～ 1085.
  • 10Shi Y, Eberhart R C. A modified particle swarm optimizer [A]. Proc IEEE Int Conf on Evolutionary Computation[C]. Anchorage, 1998: 69-73.

共引文献115

同被引文献22

引证文献3

二级引证文献42

上海电机学院学报
2007年 第2期

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