DocumentCode
509171
Title
Adaptive Population Differentiation PSO Algorithm
Author
Yang Junjie ; Xue, Liqin
Author_Institution
Sch. of Inf. & Technol., Zhanjiang Normal Univ., Zhanjiang, China
Volume
2
fYear
2009
fDate
21-22 Nov. 2009
Firstpage
388
Lastpage
391
Abstract
In order to solve the problem of easily fall into local optimal solutions, lower convergent precision, slower convergence rates and the poor population diversity, an improved PSO algorithm was proposed in this paper. The diversity was improved by the application of fuzzy clustering method. The sub-populations were classified automatically based on the feature of the population, and the information was exchanged by alliance in among the sub-populations. The simulation results of our improved PSO and indicated that the performance of optimal precision, efficiency and the stability are much better than that of traditional PSO.
Keywords
convergence; fuzzy set theory; particle swarm optimisation; pattern clustering; PSO algorithm; adaptive population differentiation; convergence rate; convergent precision; fuzzy clustering; local optimal solution; population diversity; Acceleration; Clustering algorithms; Clustering methods; Educational institutions; Evolutionary computation; Information science; Information technology; Particle swarm optimization; Particle tracking; Stability; PSO algorithm; diversity; fuzzy clustering;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on
Conference_Location
Nanchang
Print_ISBN
978-0-7695-3859-4
Type
conf
DOI
10.1109/IITA.2009.379
Filename
5369593
Link To Document