Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (35): 49-51.DOI: 10.3778/j.issn.1002-8331.2010.35.014

• 研究、探讨 • Previous Articles     Next Articles

Adaptive particle swarm optimization algorithm of nonlinear inertia weight

XU Gang1,YANG Yu-qun2,HUANG Xian-jiu1   

  1. 1.Department of Mathematics Nanchang University,Nanchang 330031,China
    2.Nanchang University,Nanchang 330047,China
  • Received:2009-05-27 Revised:2009-07-20 Online:2010-12-11 Published:2010-12-11
  • Contact: XU Gang

一种非线性权重的自适应粒子群优化算法

徐 刚1,杨玉群2,黄先玖1   

  1. 1.南昌大学 数学系,南昌 330031
    2.南昌大学,南昌 330047
  • 通讯作者: 徐 刚

Abstract: Aiming at the prematurity and non-convergence problems in particle swarm optimization algorithm,the parameters of standard PSO affecting its optimization performance is analysed and an adaptive particle swarm optimization algorithm based on nonlinear inertia weight is pointed out.During optimization,the inertia weight changes with nonlinearity along iteration times,the improved algorithm can adaptively change search speed.A compare is made with the standard Particle Swarm Optimization as well as other advanced Particle Swarm Optimization.The experimental results illustrate that the proposed algorithm has evident superiorities in search precision and convergence speed.Especially,there are evident superiorities in multi-dimension and multi-peak nonlinear optimization questions.

Key words: particle swarm optimization algorithm, nonlinearity, adaptive

摘要: 针对粒子群优化算法中出现早熟和不收敛问题,分析了基本PSO算法参数对其优化性能的影响,提出了基于非线性权重的自适应粒子群优化算法(NWAPSO)。在优化过程中,惯性权重随迭代次数非线性变化,改进的算法能使粒子自适应地改变搜索速度进行搜索,并与基本粒子群算法以及其他改进的粒子群算法进行了比较。实验结果表明,该算法在搜索精度和收敛速度等方面有明显优势。特别对于高维、多峰等复杂非线性优化问题,算法的优越性更明显。

关键词: 粒子群优化算法, 非线性, 自适应

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