[1]余琍,徐霜,万强.基于学习理论的改进粒子群优化算法[J].郑州大学学报(工学版),2019,40(02):32-37.[doi:10.13705/j.issn.1671-6833.2019.02.007]
　Xu Shuang,Wanqiang,Yu Li.Improved Particle Swarm Optimization Algorithm Based on Learning Theory[J].Journal of Zhengzhou University (Engineering Science),2019,40(02):32-37.[doi:10.13705/j.issn.1671-6833.2019.02.007]
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基于学习理论的改进粒子群优化算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年02期
页码:
32-37
栏目:
出版日期:
2019-03-19

文章信息/Info

Title:
Improved Particle Swarm Optimization Algorithm Based on Learning Theory
作者:
余琍; 徐霜; 万强
1. 武汉大学高新技术产业发展部;2. 武汉大学计算机学院
Author(s):
Xu Shuang 1; Wanqiang 2; Yu Li 2
1. Department of High-tech Industry Development, Wuhan University 2. School of Computer Science, Wuhan University
关键词:
粒子群算法; 最优化; 有效性; 测试函数
Keywords:
Particle swarm algorithm; optimize; validity; test function
DOI:
10.13705/j.issn.1671-6833.2019.02.007
文献标志码:
A
摘要:
论文针对粒子群算法容易陷入局部最优的问题，提出基于学习理论的粒子群算法（L-PSO）。该算法通过为粒子群全局最准测试函数集上的测试证明了该算法的有效性。
Abstract:
Since PSO algorithm was easy to get trapped into local optimum,in this paper, based on the learning theory a mew PSO algorithm named as L-PSO was proposed. In L-PSO ,an integer value was set as the maximum cycle limitation for the global best particles,and propose a clustering grouping mutation mechanusm which could devude the particles into some sub-swarms,Then the competitive particle was used to replace the global optimum particle which could help jump out of the local optimum and improve the convergence speed. Experimental results on several benchmark test functions showed that L-PSO was very effective.

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更新日期/Last Update: 2019-03-24