具有高斯扰动的局部引导粒子群优化算法

J4 ›› 2016, Vol. 38 ›› Issue (06): 1183-1192.

具有高斯扰动的局部引导粒子群优化算法

吴润秀1,孙辉1,朱德刚2，赵嘉1

(1.南昌工程学院信息工程学院，江西南昌 330099；2.安徽医科大学第一附属医院，安徽合肥 230022)

收稿日期:2015-06-29 修回日期:2015-08-11 出版日期:2016-06-25 发布日期:2016-06-25
基金资助:
国家自然科学基金（61261039 ）；江西省教育厅落地计划项目( KJLD13096)；江西省自然科学基金（20122BAB201043）；江西省教育厅科技项目（GJJ13763）

A particle swarm optimization algorithm
based on local guidance and Gauss perturbation

WU Runxiu1,SUN Hui1,ZHU Degang2,ZHAO Jia1

(1.School of Information Engineering,Nanchang Institute of Technology,Nanchang 330099;
2.The First Affiliated Hospital of Anhui Medical University，Hefei 230022，China)

Received:2015-06-29 Revised:2015-08-11 Online:2016-06-25 Published:2016-06-25

摘要/Abstract

摘要：

为解决粒子群优化算法PSO存在的早熟收敛问题，提出了一种具有高斯扰动的局部引导粒子群优化算法（LGPSO）。该算法在粒子的速度更新公式上采取两种措施改进PSO：一是移除社会认知部分，使粒子仅受局部引导；二是增加全局最优粒子控制的高斯扰动项。两种改进措施相结合，可有效解决早熟收敛的问题，加快收敛的速度。本文算法通过与经典及新近改进PSO算法的多次对比实验测试，均展现出较好的寻优性能及稳定性。两种改进措施的效果分析实验测试数据和社会认知项与高斯扰动项的对比实验测试数据也进一步验证了本文算法的有效性。

关键词: 粒子群优化算法, 高斯扰动, 局部引导, 局部极值点, 社会认知

Abstract:

In order to solve the problem of low convergence rate and premature convergence of the particle swarm optimization (PSO), we propose an improved PSO algorithm which is based on local guidance and Gauss perturbation. Two measures on the particle velocity updating formula are proposed to improve the PSO. Firstly, social cognition is removed and the particles are only locally guided. Secondly, the Gauss perturbation term controlled by the global optimal particle is added. The combination of the two improvement measures can avoid the premature convergence problem and accelerate the convergence speed. Comparative experiments show that the improved PSO algorithm achieves better performance and stability than the classic PSO algorithm. Effect analysis experiments on the two improved measures and the comparative experiments on Gauss perturbation and social cognition further verify the effectiveness of the proposed algorithm.

Key words: particle swarm optimization algorithm (PSO);Gauss perturbation;local guidance;local extremum point;social cognition

吴润秀1,孙辉1,朱德刚2，赵嘉1 . 具有高斯扰动的局部引导粒子群优化算法[J]. J4, 2016, 38(06): 1183-1192.

WU Runxiu1,SUN Hui1,ZHU Degang2,ZHAO Jia1. A particle swarm optimization algorithm
based on local guidance and Gauss perturbation [J]. J4, 2016, 38(06): 1183-1192.

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