Abstract
This paper presents a variant of quantum-behaved particle swarm optimization (QPSO) that we call the comprehensive learning quantum-behaved particle swarm optimization (CLPSO), which uses a novel learning strategy whereby all other particles’ historical best information is used to update a particle’s local best position. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. The proposed QPSO variants also maintain the mean best position of the swarm as in the previous QPSO to make the swarm more efficient in global search. The experiment results on benchmark functions show that CLQPSO has stronger global search ability than QPSO and standard PSO.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Int. Conf. Neural Networks, pp. 1942–1948 (1995)
Van den Bergh, F.: An Analysis of Particle Swarm Optimizers. University of Pretoria, South Africa (2001)
Sun, J., Feng, B., Xu, W.B.: Particle Swarm Optimization with Particles Having Quantum Behavior. In: Proc. 2004 Congress on Evolutionary Computation, Piscataway, NJ, pp. 325–331 (2004)
Sun, J., Xu, W.B., Feng, B.: A Global Search Strategy of Quantum-behaved Particle Swarm Optimization. In: Proc. 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, pp. 111–115 (2004)
Sun, J., Xu, W.B., Feng, B.: Adaptive Parameter Control for Quantum-behaved Particle Swarm Optimization on Individual Level. In: Proc. 2005 IEEE International Conference on Systems, Man and Cybernetics, pp. 3049–3054 (2005)
Sun, J., Lai, C.-H., Xu, W.-B., Ding, Y., Chai, Z.: A Modified Quantum-Behaved Particle Swarm Optimization. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4487, pp. 294–301. Springer, Heidelberg (2007)
Shi, Y., Eberhart, R.C.: A Modified Particle Swarm. In: Proc. 1998 IEEE International Conference on Evolutionary Computation, Piscataway, NJ, pp. 69–73 (1998)
Shi, Y., Eberhart, R.C.: Empirical Study of Particle Swarm Optimization. In: Proc. 1999 Congress on Evolutionary Computation, Piscataway, NJ, pp. 1945–1950 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this chapter
Cite this chapter
Long, H., Zhang, X. (2012). Quantum-Behaved Particle Swarm Optimization Based on Comprehensive Learning. In: Jin, D., Lin, S. (eds) Advances in Electronic Commerce, Web Application and Communication. Advances in Intelligent and Soft Computing, vol 149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28658-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-28658-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28657-5
Online ISBN: 978-3-642-28658-2
eBook Packages: EngineeringEngineering (R0)