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Soft Computing
Erschienen in: Soft Computing 6/2012

01.06.2012 | Original Paper

An improved cooperative quantum-behaved particle swarm optimization

verfasst von: Yangyang Li, Rongrong Xiang, Licheng Jiao, Ruochen Liu

Erschienen in: Soft Computing | Ausgabe 6/2012

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Abstract

Particle swarm optimization (PSO) is a population-based stochastic optimization. Its parameters are easy to control, and it operates easily. But, the particle swarm optimization is a local convergence algorithm. Quantum-behaved particle swarm optimization (QPSO) overcomes this shortcoming, and outperforms original PSO. Based on classical QPSO, cooperative quantum-behaved particle swarm optimization (CQPSO) is present. This CQPSO, a particle firstly obtaining several individuals using Monte Carlo method and these individuals cooperate between them. In the experiments, five benchmark functions and six composition functions are used to test the performance of CQPSO. The results show that CQPSO performs much better than the other improved QPSO in terms of the quality of solution and computational cost.
Vorheriger Artikel On mean shift-based clustering for circular data
Nächster Artikel A mathematical analysis of the long-run behavior of genetic algorithms for social modeling

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Metadaten
Titel
An improved cooperative quantum-behaved particle swarm optimization
verfasst von
Yangyang Li
Rongrong Xiang
Licheng Jiao
Ruochen Liu
Publikationsdatum
01.06.2012
Verlag
Springer-Verlag
Erschienen in
Soft Computing / Ausgabe 6/2012
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-012-0803-y

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