DocumentCode :
2723411
Title :
Statistics Based Model Evaluation and Parameter Selection for Particle Swarm Optimization Algorithm
Author :
Hu, Bixin
Author_Institution :
Coll. of Comput. Sci., Yangtze Univ., Jingzhou, China
fYear :
2012
fDate :
11-13 Aug. 2012
Firstpage :
1766
Lastpage :
1769
Abstract :
It is proper that to evaluate algorithm´s performance using statistics for stochastic search optimization such as PSO. In this paper, we do performance statistics and analysis for some different situation taking Rosen Brock function as example, from statistics we think that local model is better than global model in avoiding premature, and neighborhood size is not important, number of particles should be enough large to distributed as uniformly as possible in search space, and the same times it should smaller than iteration times to complete information flow among particles. Based on this statistics we present a weighted PSO model, test result shows that our model´s performance is better than basic PSO model.
Keywords :
particle swarm optimisation; search problems; statistical analysis; stochastic processes; PSO; Rosen Brock function; particle swarm optimization algorithm; search space; statistics based model evaluation selection; statistics based model parameter selection; stochastic search optimization; Algorithm design and analysis; Analytical models; Computational complexity; Computational modeling; Convergence; Optimization; Particle swarm optimization; PSO; Rosenbrock; performance analysis; statistics; weighted PSO;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science & Service System (CSSS), 2012 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-0721-5
Type :
conf
DOI :
10.1109/CSSS.2012.441
Filename :
6394760
Link To Document :
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