Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (2): 240-242.DOI: 10.3778/j.issn.1002-8331.2010.02.071

• 工程与应用 • Previous Articles     Next Articles

Model updating based on chaos quantum-behaved particle swarm optimization algorithm

QIN Yu-ling1,KONG Xian-ren1,LUO Wen-bo2   

  1. 1.Research Center of Satellite Technology,Harbin Institute of Technology,Harbin 150001,China
    2.China Academy of Space Technology,Beijing 100086,China
  • Received:2009-09-11 Revised:2009-10-12 Online:2010-01-11 Published:2010-01-11
  • Contact: QIN Yu-ling

混沌量子粒子群算法在模型修正中的应用

秦玉灵1,孔宪仁1,罗文波2   

  1. 1.哈尔滨工业大学 卫星技术研究所,哈尔滨 150001
    2.中国空间技术研究院,北京 100086
  • 通讯作者: 秦玉灵

Abstract: The Chaos-Particle Swarm Optimization algorithm(CPSO) and the Quantum-behaved Particle Swarm Optimization algorithm(QPSO) improve the search quantity of the standard Particle Swarm Optimization algorithm(PSO) to some extent,but they still have low convergence rate and are easy to get the local minimum value.The Chaos Quantum-behaved Particle Swarm Optimization algorithm(CQPSO) introduces the chaos search mechanism into the QPSO,which improves the search efficiency and the calculation quanlity.The PSO,CPSO,QPSO,CQPSO are used to update a plate,and results show that the CQPSO has higher search efficiency and isn’t easy to be trapped in the local optimal value,the Finite Element Model(FEM) updated by the CQPSO has higher updating precision than others.

Key words: convergence velocity, local minimum, Chaos Quantum-behaved Particle Swarm Optimization(CQPSO), model updating

摘要: 混沌粒子群算法和量子粒子群算法在一定程度上改进了标准粒子群算法的搜索质量,但两者仍存在收敛速度慢、易陷入局部极小等问题。混沌量子粒子群算法将混沌搜索机制引入量子粒子群算法,提高了搜索效率和计算质量。用粒子群算法、混沌粒子群算法、量子粒子群算法和混沌量子粒子群算法对一平板结构进行模型修正,结果表明,混沌量子粒子群算法具有较高的搜索效率和避免陷入局部最优的能力,修正后的模型比单独采用混沌或者量子粒子群算法具有更高的修正精度。

关键词: 收敛速度, 局部极小, 混沌量子粒子群, 模型修正

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