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浙江大学学报（理学版）
浙江大学学报（理学版）  2016, Vol. 43 Issue (6): 696-700    DOI: 10.3785/j.issn.1008-9497.2016.06.014
电子科学     
基于动态因子和共享适应度的改进粒子群算法
谭熠峰, 孙婷婷, 徐新民
浙江大学 信息与电子工程学院, 浙江 杭州 310027
A modified particle swarm optimization algorithm based on dynamic learning factors and sharing method
TAN Yifeng, SUN Tingting, XU Xinming
College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China
 全文: PDF(654 KB)  
摘要： 为提高粒子群算法的收敛速度和优化性能，避免陷入局部最优，提出了一种基于动态学习因子和共享适应度函数的改进粒子群算法.在惯性权重w随着迭代次数非线性减少而动态调整学习因子的基础上，引入共享适应度函数.当算法未达到终止条件而收敛时，利用粒子和最优解间距离挑选一批粒子重新初始化形成新群体，并用共享适应度函数对新群体进行评价，新旧2个群体分别追随自己的局部最优解直至迭代结束.对4个典型多峰复杂函数的测试结果表明，该改进算法不仅加快了寻得最优解的速度，而且提高了粒子群算法全局收敛的性能.
关键词： 动态;  学习因子;  共享适应度;  粒子群算法    
Abstract: To improve the global convergence ability and rate of particle swarm optimization, an improved particle swarm optimization algorithm based on dynamic learning factors and sharing method is proposed. The inertia weight factor of the algorithm decreases non-linearly, and the learning factor changes dynamically with the descending. A sharing fitness function is introduced on the basis of dynamic regulation. When the algorithm is stagnated without reaching termination, part of the particles will be selected according to the distance between particles and optimal solution. The chosen particles will be re-initialized as a new swarm and be evaluated by sharing fitness. The old and new swarms follow their own local solutions respectively until the end of the iteration. Simulation results of four typical multimodal functions show that the modified algorithm can greatly enhance the rate of the optimal solution searching and improve the global convergence performance of PSO.
Key words: dynamic    learning factor    sharing fitness    particle swarm optimization
收稿日期: 2014-03-04 出版日期: 2017-03-07
CLC:  TP301.6  
基金资助: 浙江省公益技术研究工业项目(2015C31073).
通讯作者: 徐新民,http://orcid.org/0000-0002-0910-2375,E-mail:xuxm@zju.edu.cn     E-mail: xuxm@zju.edu.cn
作者简介: 谭熠峰(1991-),ORCID:http://orcid.org/0000-0003-1151-9206,男,硕士研究生,主要从事嵌入式研究.
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引用本文:

谭熠峰, 孙婷婷, 徐新民. 基于动态因子和共享适应度的改进粒子群算法[J]. 浙江大学学报（理学版）, 2016, 43(6): 696-700.

TAN Yifeng, SUN Tingting, XU Xinming. A modified particle swarm optimization algorithm based on dynamic learning factors and sharing method. Journal of ZheJIang University(Science Edition), 2016, 43(6): 696-700.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2016.06.014    或    https://www.zjujournals.com/sci/CN/Y2016/V43/I6/696

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