Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (14): 65-66.

• 理论研究 • Previous Articles     Next Articles

Comparison and analysis of particle swarm optimization method based on genetic operator

LEI Xiu-juan1,2,SHI Zhong-ke2,SUN Gui-qi1   

  1. 1.College of Computer Science,Shaanxi Normal University,Xi’an 710062,China
    2.College of Automation,Northwestern Polytechnical University,Xi’an 710072,China
  • Received:2007-08-31 Revised:2007-11-12 Online:2008-05-11 Published:2008-05-11
  • Contact: LEI Xiu-juan

基于遗传算子的粒子群优化算法的比较分析

雷秀娟1,2,史忠科2,孙瑰琪1   

  1. 1.陕西师范大学 计算机科学学院,西安 710062
    2.西北工业大学 自动化学院,西安 710072
  • 通讯作者: 雷秀娟

Abstract: To analyse the performance of improved particle swarm optimization method deeply,three strategies are designed to experiment several standard test functions optimization problem.One of the strategies is linear inertia weight reduction,the second is the PSO with genetic operator,and the third is rejoining the constriction factor based on the second strategy.Through the optimization and simulation of the test functions in MATLAB 7.0 software,the performance of the PSO mixed the genetic operator and constriction factor is best of all.

Key words: Particle Swarm Optimization(PSO), Linearly Decreasing Weight(LDW), genetic operator, constriction factor

摘要: 为了深入分析探讨改进的粒子群优化算法的性能,针对典型的函数优化问题,设计了3种方案:(1)采用线性递减惯性权重的PSO;(2)基于遗传算子的PSO;(3)在方案(2)基础上,加入收缩因子χ。在MATLAB 7.0中对常用的测试函数进行优化仿真,发现当融合遗传算子和收缩因子时,算法性能最优。

关键词: 粒子群优化, 线性递减惯性权重, 遗传算子, 收缩因子