Title :
Adaptive Population Differentiation PSO Algorithm
Author :
Yang Junjie ; Xue, Liqin
Author_Institution :
Sch. of Inf. & Technol., Zhanjiang Normal Univ., Zhanjiang, China
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;
Conference_Titel :
Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on
Conference_Location :
Nanchang
Print_ISBN :
978-0-7695-3859-4
DOI :
10.1109/IITA.2009.379