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一种自适应粒子群算法 被引量：6

Lanscape Adaptive Particle Swarm Optimizer
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摘要 基于对不同粒子群算法(PSO)中惯性权重、全局收敛性、收敛精度和速度的分析,提出了一种新的全局最优值自适应变化的粒子群算法(LAPSO)。并采用该方法对三种不同的基准函数进行了测试,将LAPSO测试结果与典型的收敛粒子群算法(LKPSO)和扩散粒子群算法(LWPSO)进行了比较。结果表明:自适应粒子群算法具有收敛速度快、进化精度高的特点,是一种新型全局收敛粒子群算法。 Based on the analysis of inertia weight, global convergence and convergent speed as well as accuracy, a novel adaptive particle swarm optimization （LAPSO） was proposed. This landscape method was applied to investigate three different Benchmark functions. Compared with the experimental results of LWPSO and LKPSO, the results show that LAPSO is more performance in speed and accuracy convergence. Therefore, LAPSO is a global particle swarm optimization.
作者 李旭芳 王士同
机构地区 江南大学信息工程学院
出处 《系统仿真学报》 CAS CSCD 北大核心 2009年第9期2582-2585,共4页 Journal of System Simulation
基金 教育部重点科学研究项目(105087) 国防应用基础研究基金项目(A1420061266)
关键词 粒子群算法 惯性权重 全局最优值 局部收敛 particle swarm optimization global optimality convergence local convergence
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
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参考文献13

  • 1EBERHART R, KENNEDY J. A new optimizer using particle swarm theory [C]//Proc of sixth Int Sym on Micro Machine and Human Science, Nagoya, Japan, 1995. pp. 39-43.
  • 2CHATTERJEE A, SIARRY P. Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization [J]. Computer & Operations Research (S0305-0548), 2006, 33(3): 859-871.
  • 3LEONTITSIS A, KONTOGIORGOS D, PAGGE J. Repel the swarm to the optimum [J]. Apple. Math. Comput(S0096-3003), 2006, 173(11): 265-273.
  • 4曾建潮,崔志华.一种保证全局收敛的PSO算法[J].计算机研究与发展,2004,41(8):1333-1338. 被引量：158
  • 5HEO J S, K. LEE Y, RAMIREZ R. Multiobjective control of power plants using particle swarm optimization techniques [J]. IEEE Transactions on Energy Concersion (S0885-8969), 2006, 21(2): 552-561.
  • 6GILL M K, KAHEIL Y H, MCKEE M, et al. Multiobjective particle swarm optimization for parameter estimation in hydrology [J]. Water Resources Research (S0043-1397), 2006, 42(7): 417-514.
  • 7XUE Y, JIN Y S, JIANG Y Y, et al. A modified particle swarm optimizer with dynamic adaptation [J]. Applied Mathematics and Computation (S0096-3003), 2007, 189(2): 1205-1213.
  • 8SHI Y, EBERHART R C. Empirical study of partical swarm optimization [C]// Proceeding of Congress on Evolutionary Computation. Piscatway, NJ, USA: IEEE Service Center, 1999: 1945-1950.
  • 9CLERC M. The swarm and the queen: toward a deterministic and adaptive particle swarm optimization [C]// Proceeding of the Congress of Evolutionary Computation, Washington, DC, 1999, 3(9): 1951-1957.
  • 10RATNAWEERA A, HALGAMUGE S K. Self-organizing hierarchical partical swarm optimizer with time-varying acceleration coefficients [J]. IEEE Transaction on Evolutionary Computation (S1089-778X), 2004, 8(3): 240-255.

二级参考文献14

  • 1P N Suganthan. Particle swarm optimiser with neighbourhood operator. In: Proc of the Congress on Evolutionary Computation.Piscataway, NJ: IEEE Service Center, 1999. 1958～1962
  • 2E Ozcan, C Mohan. Particle swarm optimization: Surfing the waves. In: Proc of the Congress on Evolutionary Computation.Piscataway, NJ: IEEE Service Center, 1999. 1939～1944
  • 3M Clerc, J Kennedy. The particle swarm: Explosion, stability and convergence in a multi-dimensional complex space. IEEE Trans on Evolutionary Computation, 2002, 6(1): 58～73
  • 4F Solis, R Wets. Minimization by random search techniques.Mathematics of Operations Research, 1981, 6(1 ): 19～ 30
  • 5F Van den Bergh. An analysis of particle swarm optimizers: [ Ph D dissertation]. Pretoria: University of Pretoria, 2001
  • 6王凌.智能优化算法及其应用.北京:清华大学出版社,2001( Wang Ling. Intelligent Optimization Algorithms with Applications( in Chinese) . Beijing: Tsinghua University Press,2001)
  • 7J Holland. Adaption in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press, 1975
  • 8[1]SHEHORY O,KRAUS S.Task allocation via coalition formation among autonomous agents[A].In O Shehory ed.Proc of IJCAI-95[C].Los Angeles,CA,USA,Morgan Kaufmann Publishers,1995.
  • 9[2]SANDHOLM T,LARSON K,ANDERSSON M,et al.Anytime coalition structure generation with worst case guarantees[A].In T Sandholm ed.Proc of the National Conference on Artificial Intelligence[C].Madison,WI,1998.
  • 10[3]SANDHOLM T,LESSER V.Coalition among computationally bounded agents[J].Artificial Intelligence,1997,94(1):99-137.

共引文献169

同被引文献40

引证文献6

二级引证文献21

系统仿真学报
2009年 第9期

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