An Improve Particle Swarm Optimization Algorithm Based on Learning Theory
[1]Zhang Weiwei,Gao Kui,Zhang Weizheng,et al.An Improve Particle Swarm Optimization Algorithm Based on Learning Theory[J].Journal of Zhengzhou University (Engineering Science),2019,40(02):26-31.[doi:10.13705/j.issn.1671-6833.2018.05.018]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
40
Number of periods:
2019 02
Page number:
26-31
Column:
Public date:
2019-03-19
- Title:
- An Improve Particle Swarm Optimization Algorithm Based on Learning Theory
- Keywords:
- clone selection; optimize; biological immunity
- CLC:
- -
- Abstract:
- Since PSO algorithm was casy to get trapped into local optimum ,in this paper ,based on the learning theory a new PSO algorithm nameed as L-PSO was proposed. In L-PSO,an inger value was set as the maximum cycle limitation for the global best particles ,and propose a clustering grouping mutation mechanism which could devide the particles into some subb-swarms and generates the competitive by crossover and mutation of the teo centers selected randomly from sub-swarms.Then the competitive particle was used to replace the global optimum particle which could help jump oout of the local optimum and improve theconvergence speed. Experimenta results on several benchmark test function shoxed that L-PSO was very effective.
- References:
- -
- Similar References:
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Last Update:
2019-03-24
