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浙江大学学报(工学版)
浙江大学学报(工学版)  2018, Vol. 52 Issue (2): 367-378    DOI: 10.3785/j.issn.1008-973X.2018.02.020
计算机技术     
基于随机维度划分与学习的粒子群优化算法
张庆科1,2, 孟祥旭1, 张化祥2, 杨波3, 刘卫国1
1. 山东大学 计算机科学与技术学院, 教育部数字媒体技术工程研究中心, 山东 济南 250101;
2. 山东师范大学, 信息科学与工程学院, 山东 济南 250014;
3. 山东省网络环境智能计算技术重点实验室, 山东 济南 250012
Particle swarm optimization based on random vector partition and learning
ZHANG Qing-ke1,2, MENG Xiang-xu1, ZHANG Hua-xiang2, YANG Bo3, LIU Wei-guo1
1. School of Computer Science and Technology, Engineering Research Center of Digital Media Technology, Ministry of Education, Shandong University, Jinan 250101, China;
2. School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China;
3. Shandong Provincial Key Laboratory of Network based Intelligent Computing, University of Jinan, Jinan 250022, China
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摘要：

针对粒子群优化算法在搜索过程存在的种群多样性低和过早收敛问题，提出基于随机维度划分与学习的新型粒子群优化算法（RVPLO）.该算法将每个粒子的维度随机划分为多个不同的子段，每个子段随机分配一种学习算子（中心学习算子或离散学习算子），通过学习算子实现对各子段内的维度数值更新操作.中心学习算子用以加强粒子的全局搜索能力，离散学习算子用以加强粒子的局部搜索能力.粒子维度划分策略实现了将高维优化问题转化为低维优化问题，降低了优化问题求解的难度.粒子随机维度划分和算子随机分配的双重动态调节机制使得算法具备求解复杂单峰函数，多峰函数优化问题的能力.实验测试结果及显著性统计结果表明，RVPLO算法同其他8个经典改进算法相比，在单峰函数，多峰等函数优化中具有收敛速度快，求解精度高的优势.
Abstract:

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.
收稿日期: 2016-12-13 出版日期: 2018-03-09
CLC:  TP301  
基金资助:

国家自然科学基金资助项目（61572230，61173078，61573166，61572298，61772322）.
通讯作者: 刘卫国,男,教授.orcid.org/0000-0001-8834-0453.     E-mail: weiguo.liu@sdu.edu.cn
作者简介: 张庆科(1985-),男,讲师,从事计算智能,高性能计算等研究.orcid.org/0000-0003-3960-172X,E-mail:tsingke@sdnu.edu.cn
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引用本文:

张庆科, 孟祥旭, 张化祥, 杨波, 刘卫国. 基于随机维度划分与学习的粒子群优化算法[J]. 浙江大学学报(工学版), 2018, 52(2): 367-378.

ZHANG Qing-ke, MENG Xiang-xu, ZHANG Hua-xiang, YANG Bo, LIU Wei-guo. Particle swarm optimization based on random vector partition and learning. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(2): 367-378.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2018.02.020    或    http://www.zjujournals.com/eng/CN/Y2018/V52/I2/367

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