采用扰动加速因子的自适应粒子群优化算法

doi:10.3969/j.issn.1001-2400.2012.04.014

J4 ›› 2012, Vol. 39 ›› Issue (4): 74-80.doi: 10.3969/j.issn.1001-2400.2012.04.014

采用扰动加速因子的自适应粒子群优化算法

姜建国¹;田旻¹;王向前²;龙秀萍¹;李锦¹

(1. 西安电子科技大学计算机学院，陕西西安 710071；
2. 平顶山学院师范教育学院，河南平顶山 467002)

收稿日期:2011-04-08 出版日期:2012-08-20 发布日期:2012-10-08
通讯作者: 姜建国
作者简介:姜建国(1956-)，男，教授，E-mail: jgjiang@mail.xidian.edu.cn．
基金资助:
国家部委基础科研计划资助项目(D1120060967)

Adaptive particle swarm optimization via disturbing acceleration coefficents

JIANG Jianguo¹;TIAN Min¹;WANG Xiangqian²;LONG Xiuping¹;LI Jin¹

(1. School of Computer Science and Technology, Xidian Univ., Xi'an 710071， China;
2. Pingdingshan Univ. Normal Edu. Faculty, Pingdingshan 467002， China)

Received:2011-04-08 Online:2012-08-20 Published:2012-10-08
Contact: JIANG Jianguo

摘要/Abstract

摘要：

针对粒子群算法容易陷入早熟收敛和搜索效率不高等问题，分析了几个现有的改进粒子群优化算法．在粒子对称分布有利于提高搜索结果的基础上，对粒子群优化算法进行了改进．改进后的算法可以在运行过程中的不同阶段自适应地以余弦函数的变化方式调整惯性权重系数;在加速因子线性变化的基础上，基于一定的条件对加速因子进行扰动;并确定了相应条件参数的参数取值．通过几个经典的函数，对该算法进行了验证，并与相关文献中改进的粒子群优化算法进行了对比．结果表明，新算法不仅显著提高了收敛速度，而且能有效地改善早熟现象．

关键词: 粒子群优化, 加速因子, 惯性权重系数

Abstract:

The Particle Swarm Optimization (PSO) is an evolutionary method which is used to search for the global optimal solution by iteration. However, PSO has the problem that the particle swarm algorithm falls easily into premature convergence and has a low search efficiency. In this paper, after analyzing several existing improved particle swarm algorithms, a new improved particle swarm algorithm is proposed based on the fact that symmetrical particles distribution can enhance the optimation search results. The proposed algorithm can adjust the inertia weight factor adaptively in different phases of the process according to the variation of the cosine function. In addition, the acceleration coefficents based on linear variation are disturbed under a certain condition. Moreover, an appropriate value of the parameter in this condition is determined via experiments. Several classic functions have been used to test this new algorithm and then the results of this new algorithm are analyzed by comparing it with several relevant algorithms in the literature. The results show that this new algorithm can not only improve the convergence speed significantly, but also improve the premature convergence phenomenon.

Key words: particle swarm optimization, acceleration coefficents, inertia weight factor

中图分类号:

TP301.6

姜建国;田旻;王向前;龙秀萍;李锦. 采用扰动加速因子的自适应粒子群优化算法[J]. J4, 2012, 39(4): 74-80.

JIANG Jianguo;TIAN Min;WANG Xiangqian;LONG Xiuping;LI Jin. Adaptive particle swarm optimization via disturbing acceleration coefficents[J]. J4, 2012, 39(4): 74-80.

参考文献

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采用扰动加速因子的自适应粒子群优化算法

Adaptive particle swarm optimization via disturbing acceleration coefficents

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