Abstract
In this paper, the mutation mechanism is introduced into Quantum-behaved Particle Swarm Optimization (QPSO) to increase the diversity of the swarm and then effectively escape from local minima to increase its global search ability. Based on the characteristic of QPSO algorithm, the two variables, global best position (gbest) and mean best position (mbest), are mutated with Cauchy distribution respectively. Moreover, the amend strategy based on annealing is adopted by the scale parameter of mutation operator to increase the self-adaptive capability of the improved algorithm. The experimental results on test functions showed that QPSO with gbest and mbest mutation both performs better than PSO and QPSO without mutation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE Int. Conf. on Neural Network, pp. 1942–1948 (1995)
Angeline, P.J.: Evolutionary Optimizaiton Versus Particle Swarm Opimization: Philosophyand Performance Differences. In: Rothermel, K., Hohl, F. (eds.) MA 1998. LNCS, vol. 1477, pp. 601–610. Springer, Heidelberg (1998)
Eberhart, R.C., Shi, Y.: Comparison between Genetic Algorithm and Particle Swarm Optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 611–616. Springer, Heidelberg (1998)
Krink, T., Vesterstrom, J., Riget, J.: Particle Swarm Optimization with Spatial Particle Extension. In: IEEE Proceedings of the Congress on Evolutionary Computation (2002)
Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization. In: Proceedings of the IEEE Conference on Evolutionary Computation, ICEC, pp. 84–89 (1998)
Lovbjerg, M., Rasussen, T.K., Krink, T.: Hybrid Particle Swarm Optimiser with Breeding and Subpopulations. In: Proc.of the third Genetic and Evolutionary Computation Conferences (2001)
Kennedy, J.: Bare Bones Particle Swarms. In: IEEE Swarm Intelligence Symposium, pp. 80–87 (2003)
Sun, J., Feng, B., Xu, W.: Particle Swarm Optimization with Particles Having Quantum Behavior. In: IEEE Proc. of Congress on Evolutionary Computation, pp. 325–331 (2004)
Shi, Y., Eberhart, R.: Empirical Study of Particle Swarm Optimization. In: Proc. Congress on Evolutionary Computation, pp. 1945–1950 (1999)
Clerc, M., Kennedy, J.: The Particle Swarm: Explosion, Stability and Convergence in a Multi-Dimensional Complex Space. IEEE Transaction on Evolutionary Computation, 58–73 (2002)
Sun, J., et al.: A Global Search Strategy of Quantum-behaved Particle Swarm Optimization. In: Proceedings of IEEE conference on Cybernetics and Intelligent Systems, pp. 111–116 (2004)
Yao, X., Liu, Y.: Fast Evolutionary Strategies. In: Proc. 6th Conf. Evolutionary Programming, pp. 151–161 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, J., Sun, J., Xu, W. (2006). Quantum-Behaved Particle Swarm Optimization with Adaptive Mutation Operator. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_126
Download citation
DOI: https://doi.org/10.1007/11881070_126
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
eBook Packages: Computer ScienceComputer Science (R0)