计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (3): 51-53.DOI: 10.3778/j.issn.1002-8331.2010.03.016

• 研究、探讨 • 上一篇    下一篇

一种基于混沌的自适应粒子群全局优化方法

高雷阜,刘旭旺   

  1. 辽宁工程技术大学 数学与系统科学研究所,辽宁 阜新 123000
  • 收稿日期:2008-09-17 修回日期:2008-11-07 出版日期:2010-01-21 发布日期:2010-01-21
  • 通讯作者: 高雷阜

Adaptive particle swarm global optimization algorithm based on chaos

GAO Lei-fu,LIU Xu-wang   

  1. Institute of Mathematics and Systems Science,Liaoning Technical University,Fuxin,Liaoning 123000,China
  • Received:2008-09-17 Revised:2008-11-07 Online:2010-01-21 Published:2010-01-21
  • Contact: GAO Lei-fu

摘要: 充分利用粒子群优化算法的收敛速度较快及混沌运动的遍历性、随机性以及对初值的敏感性等特性,考虑到惯性因子对多样性的影响,通过引入早熟收敛程度评价机制,采用逻辑自映射函数来产生混沌序列,提出一种基于混沌思想的自适应混沌粒子群优化(ACPSO)算法,改善了粒子群优化算法摆脱局部极值点的能力,提高了算法的收敛速度和精度。仿真结果表明提出的自适应混沌粒子群优化算法的性能明显优于一般混沌粒子群优化算法。

关键词: 非线性规划, 全局优化, 粒子群优化, 混沌优化, 自适应

Abstract: By making the best of the particle swarm optimization’s quick convergence speed and the ergodicity,stochastic property and regularity of chaos,considering the effect of inertia gene for diversity,introducing the evaluate mechanism about precocity convergence’s degree,getting chaos sequence using logic mapped function, this paper proposes an adaptive particle swarm global optimization algorithm based on Chaos.The experimental results demonstrate that the new algorithm has the ability to avoid being trapped in local minima,and improves computational precision,convergence speed and the ability of global optimization.The performance of ACPSO is evidently better than original PSO and CPSO.

Key words: nonlinear optimization, global optimization, particle swarm optimization, chaos optimization, adaptive

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