Particle swarm optimization based on random vector partition and learning

被引:2
|
作者
Zhang Q.-K. [1 ,2 ]
Meng X.-X. [1 ]
Zhang H.-X. [2 ]
Yang B. [3 ]
Liu W.-G. [1 ]
机构
[1] School of Computer Science and Technology, Engineering Research Center of Digital Media Technology, Ministry of Education, Shandong University, Jinan
[2] School of Information Science and Engineering, Shandong Normal University, Jinan
[3] Shandong Provincial Key Laboratory of Network based Intelligent Computing, University of Jinan, Jinan
来源
Liu, Wei-Guo (weiguo.liu@sdu.edu.cn) | 2018年 / Zhejiang University卷 / 52期
关键词
Center operator; Decenter operator; High-dimensional function optimization; Particle swarm optimization (PSO); Random vector partition;
D O I
10.3785/j.issn.1008-973X.2018.02.020
中图分类号
学科分类号
摘要
A random vector partition learning particle swarm optimization (RVPLO) was propased in order to increase the diversity of population and avoid the immature convergence. The full dimension of a particle was randomly divided into several segments and each of the segment was assigned by the centralized operator or decentralized operator to update the corresponding dimensional values. The vector partition operation decomposed a high-dimensional problem into a low-dimensional problem and reduced the solving difficulty. The random assignment of different learning operators provided multiple strategies for particles to update its positions and enriched the diversity of the population. The dual randomization mechanism by vector partition and operator assignment made it possible to solve the unimodal and multimodal problems. Comprehensive experimental results achieved by RVPLO were compared with some modified PSO algorithm. The statistical results indicate that the proposed algorithm has a higher global searching accuracy and faster convergence speed than other eight classical methods in solving the unimodal and multimodal functions. © 2018, Zhejiang University Press. All right reserved.
引用
收藏
页码:367 / 378and405
相关论文
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