Improved Particle Swarm Optimization Algorithm Based on Learning Theory
[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
40卷
Number of periods:
2019 02
Page number:
32-37
Column:
Public date:
2019-03-19
- Title:
- Improved Particle Swarm Optimization Algorithm Based on Learning Theory
- Keywords:
- Particle swarm algorithm; optimize; validity; test function
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2019.02.007
- 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.
- References:
- -
- Similar References:
Memo
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Last Update:
2019-03-24