Adaptive particle swarm optimization algorithm with dynamic acceleration factor

被引：2

作者：

Chen H. ^{[1
,2
]}

Fan Y.-R. ^{[1
]}

Deng S.-G. ^{[1
]}

机构：

[1] CNPC Key Laboratory of Well Logging in China University of Petroleum

[2] College of Mathematics and Computational Science in China University of Petroleum

来源：

Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science) | 2010年 / 34卷 / 06期

关键词：

Acceleration factor; Adaptive strategy; Particle swarm optimization; Swarm intelligence;

D O I：

10.3969/j.issn.1673-5005.2010.06.033

中图分类号：

学科分类号：

摘要：

A new adaptive particle swarm optimization algorithm with dynamical acceleration factor was proposed to improve the global search and local search balance of acceleration factor. By analyzing the variation of the two acceleration factors, the corresponding ordinary differential equation (ODE) model was developed, and the dynamic adjustment formula for acceleration factor was obtained by solving the ODE model. Several classic Benchmarks functions were tested. The results show that the acceleration factor can be automatically adjusted during the run of the algorithm. So it keeps particle's own advantages and improves searching ability of global optimum in the population at the initial generations. At a later time, social information is emphasized, and the searching ability of local optimum is improved. So the algorithm is gradually stabilized.

引用

页码：173 / 176+184

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