Abstract
Particle Swarm Optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO could often easily fall into local optima. This paper presents a hybrid PSO algorithm called RPSO by applying a new re-diversification mechanism and a dynamic Cauchy mutation operator to accelerate the convergence of PSO and avoid premature convergence. Experimental results on many well-known benchmark optimization problems have shown that the RPSO could successfully deal with those difficult multimodal functions while maintaining fast search speed on those simple unimodal functions in the function optimization.
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: IEEE International Conference on Neural Networks, Perth, Australia, IEEE Computer Society Press, Los Alamitos (1995)
Parsopoulos, K.E., Plagianakos, V.P., Magoulas, G.D., Vrahatis, M.N.: Objective Function “stretching” to Alleviate Convergence to Local Minima. Nonlinear Analysis TMA 47, 3419–3424 (2001)
Eberhart, R., Shi, Y.: Comparison between Genetic Algorithms and Particle Swarm Optimization. In: The 7th Annual Conference on Evolutionary Programming, San Diego, USA, pp. 69–73 (1998)
Hu, X., Shi, Y., Eberhart, R.: Recentt Advenes in Particle Swarm. In: Congress on Evolutionary Computation, Portland, Oregon, June 19-23, 2004, pp. 90–97 (2004)
Shi, Y., Eberhart, R.: A Modified Partilce Swarm Optimzer. In: CEC 1998. Proceedings of the IEEE Congress on Evolutionary Computation, Piscataway, NJ, pp. 69–73. IEEE Computer Society Press, Los Alamitos (1998)
van den Bergh, F., Engelbrecht, A.P.: Cooperative Learning in Neural Networks using Particle Swarm Optimization. South African Computer Journal, 84–90 (November 2000)
Xie, X., Zhang, W., Yang, Z.: Hybrid Particle Swarm Optimizer with Mass Extinction. In: ICCCAS 2002. International Conf. on Communication, Circuits and Systems, Chengdu, China, pp. 1170–1174 (2002)
Lovbjerg, M., Krink, T.: Extending Particle Swarm Optimisers with Self-Organized Criticality. Proceedings of Fourth Congress on Evolutionary Computation 2, 1588–1593 (2002)
Coelho, L.S., Krohling, R.A.: Predictive controller tuning using modified particle swarm optimization based on Cauchy and Gaussian distributions. In: Proceedings of the 8th On-Line World Conference on Soft Computing in Industrial Applications. WSC8 (2003)
Hu, X., Eberhart, R.C.: Adaptive particle swarm optimization: detection and response to dynamic systems. In: Proc. Congress on Evolutionary Computation, pp. 1666–1670 (2002)
Wang, H., Liu, Y., Li, C.H., Zeng, S.Y.: A hybrid particle swarm algorithm with Cauchy mutation. In: IEEE Swarm Intelligence Symposimu 2007. SIS 2007, Honolulu, Hawaii, USA (in press)
Blackwell, T.M.: Particle swarm and population diversity I: Analysis. Dynamic optimization problems, pp. 9–13 (2003)
Blackwell, T.M., Bentley, P.J.: Dynamic search with charged swarms. In: Langdon, W.B., et al. (eds.) Genetic and Evolutionary Computation Conference, pp. 19–26. Morgan Kaufmann, San Francisco (2002)
Janson, S., Middendorf, M.: A hierachical particle swarm optimizer for dynamic optimization problems. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 513–524. Springer, Heidelberg (2004)
Li, X., Dam, K.H.: Comparing particle swarms for tracking extrema in dynamic environments. In: Congress on Evolutionary Computation, pp. 1772–1779 (2003)
Blackwell, T.M., Branke, J.: Multi-swarm optimization in dynamic environments. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004)
Parrott, D., Li, X.: A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In: Congress on Evolutionary Computation, pp. 98–103 (2004)
Ozcan, E., Mohan, C.K.: Particle Swarm Optimization: Surfing the Waves. In: CEC 1999. Proceedings of Congress on Evolutionary Computation, Washington, DC, pp. 1939–1944 (1999)
van den Bergh, F., Engelbrecht, A.P.: Effect of Swarm Size on Cooperative Particle Swarm Optimizers. In: Genetic and Evolutionary Computation Conference, San Francisco, USA, pp. 892–899 (2001)
Feller, W.: An Introduction to Probability Theory and Its Applications, 2nd edn., vol. 2. John Wiley & Sons, Inc., Chichester (1971)
Yao, X., Liu, Y., Lin, G.: Evolutionary Programing Made Faster. IEEE Transacations on Evolutionary Computation 3, 82–102 (1999)
Veeramachaneni, K., Peram, T., Mohan, C., Osadciw, L.A.: Optimization Using Particle Swarms with Near Neighbor Interactions. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 110–121. Springer, Heidelberg (2003)
van den Bergh, F.: An Analysis of Particle Swarm Optimizers. PhD thesis, Department of Computer Science, University of Pretoria, South Africa (2002)
Zhang, W., Xie, X.: DEPSO: Hybrid particle swarm with differential evolution operator. In: IEEE Int. Conf. on System, Man & Cybernetics (SMCC), Washington, USA, pp. 3816–3821 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, H., Zeng, S., Liu, Y., Wang, W., Shi, H., Liu, G. (2007). Re-diversification Based Particle Swarm Algorithm with Cauchy Mutation. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_40
Download citation
DOI: https://doi.org/10.1007/978-3-540-74581-5_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74580-8
Online ISBN: 978-3-540-74581-5
eBook Packages: Computer ScienceComputer Science (R0)