一种改进的粒子群算法

被引:12
作者
张焱 [1 ,2 ]
高兴宝 [1 ]
机构
[1] 陕西师范大学数学与信息科学学院
[2] 西安师范学校
关键词
群体智能; 进化计算; 粒子群算法; 惯性权重;
D O I
暂无
中图分类号
TP301.6 [算法理论];
学科分类号
081202 ;
摘要
为了改进基本粒子群算法的搜索功能,针对粒子群算法易于陷入局部极值,进化后期的收敛速度慢和精度低等缺点,通过公式分析得到新的惯性权重调节方法,提出了一种新的改进粒子群算法。用几个经典测试函数进行实验,实验结果表明,新算法不仅具有更好的收敛精度,而且能更有效地进行全局搜索。
引用
收藏
页码:58 / 59+93 +93
页数:3
相关论文
共 11 条
[1]  
A particle swarm model for tracking multi-ple peaks in a dynamic environment using speciation. Parrott D,Li Xiao-dong. Proc of Congress on Evolutionary Computation . 2004
[2]  
A dynamic inertia weight particle swarm optimization algorithm. Jiao Bin,Lian Zhi-gang,Gu Xing-sheng. Chaos,Solitons and Frac-tals . 2008
[3]  
Particle Swarm Optimization. In: Proc. of IEEE International Conference on Neural Networks. Kennedy, J,and Eberhart, R. C. . 1995
[4]  
Computational Intelligence PC Tools. Eberhart RC,Simpson PK,Dobbins RW. . 1996
[5]  
Japan[P]. HARRY CHISLET.中国专利:US1357688A,1920-11-02
[6]  
Self-organizing hierarchicalparticle swarm optimizer with time-varying acceleration coef-ficients. Ratnaweera A,Halgamuge S K. IEEE Transactions on Evolutionary Computation . 2004
[7]  
Empirical study of particle swarm optimization.Proc. Of the Congress on Evolutionary Computation. Shi Yuhui,Eberhart R. Washington DC . 1999
[8]  
The particle swarm:Social adaptation of knowledge. Kennedy J. Proc IEEE International Conference on Evolutionary Computation . 1997
[9]  
A modified particle swarm optimizer. Shi Y,Eberhart R C. Proc of the IEEE Int’l Conf of Evolutionary Computation . 1998
[10]  
Particle swarm optimization with particles having quantum behavior. Sun Jun,Feng Bin,Xu Wen-bo. Proc of Congress on Evolu-tionary Computation . 2004