Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (2): 31-33.DOI: 10.3778/j.issn.1002-8331.2010.02.010

• 研究、探讨 • Previous Articles     Next Articles

Modified particle swarm optimization algorithm

ZHAO Ya-min,XU Jia-dong   

  1. School of Electronics and Information,Northwestern Polytechnical University,Xi’an 710129,China
  • Received:2009-01-17 Revised:2009-02-27 Online:2010-01-11 Published:2010-01-11
  • Contact: ZHAO Ya-min

一种改进的微粒群优化算法

赵亚敏,许家栋   

  1. 西北工业大学 电子信息学院,西安 710129
  • 通讯作者: 赵亚敏

Abstract: Based on the prominent effect of inertia weight to the performance of particle swarm optimization,a new PSO method with dynamically adjusted inertia weight is proposed. In this improved PSO method,the inertia weight is controlled by PSO optimization. Numerical results show the effectiveness of the proposed algorithm. Either to two dimension or to high dimension,both the accuracy and precision are improved. Especially for high dimension,performance of the algorithm is bettered distinctly.

Key words: Particle Swarm Optimization(PSO), inertia weight, optimization performance

摘要: 基于惯性权重对微粒群优化算法(Particle Swarm Optimization,PSO)优化性能的显著影响,提出了一种改变惯性权重的方法以改进PSO算法的优化性能。算法中惯性权重的动态改变是通过对其进行PSO寻优来控制的。经过对标准函数的测试计算,无论是二维还是多维的问题,这种改变惯性权重的PSO算法的寻优结果的准确度和精度均得以提高,收到了良好的效果,尤其在高维情况下,显示出算法性能得到了明显改善。

关键词: 微粒群优化, 惯性权重, 算法性能

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