DocumentCode :
2918602
Title :
A Novel Method for Finding Global Best Guide for Multiobjective Particle Swarm Optimization
Author :
Jiang, Qing ; Li, Jian
Author_Institution :
Inst. of Intell. Machines, Chinese Acad. of Sci., Hefei, China
Volume :
3
fYear :
2009
fDate :
21-22 Nov. 2009
Firstpage :
146
Lastpage :
150
Abstract :
The Elitism, which is the mechanism to incorporate external useful solutions in MOEA, is popular technology. One of the focus of research about elitism is how to maintain and select the global guide in order to keep the results of algorithm convergence and diversity. In this paper, a novel method to maintain elitism archive and select global guide is proposed, which divide the non-dominated solutions in the elitism archive to two kind: convergence solution and diversity solution and provides the particle angle division to manage it. The MOPSO algorithm based on the new method is compared with other multi-objective evolutionary algorithm on three complicated benchmark multi-objective function optimization problems. It is shown from the results that the Pareto front obtained with the MOPSO has good convergence and diversity.
Keywords :
Pareto optimisation; convergence; evolutionary computation; particle swarm optimisation; Elitism; MOPSO algorithm; Pareto front; algorithm convergence; convergence solution; diversity solution; global guide; multiobjective evolutionary algorithm; multiobjective function optimization problems; multiobjective particle swarm optimization; particle angle division; Birds; Computer science; Computer science education; Constraint optimization; Design optimization; Evolutionary computation; Information technology; Machine intelligence; Optimization methods; Particle swarm optimization; Elitism; Evolutionary Compuation; Multiobjective Optimization; Particle Swarm Optimization;
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.2009.23
Filename :
5369500
Link To Document :
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