Abstract
Heuristic approaches already proved their efficiency for the cases where real-world problems dynamically change in time and there is no effective way of prediction of the changes. Among them a mixed multi-swarm optimization (mSO) is regarded as the most efficient. The approach is a hybrid solution and it is based on two types of particle swarm optimization (PSO): pure PSO and quantum swarm optimization (QSO). Both types are applied in a set of simultaneously working sub-swarms. In spite of the fact that there appeared a series of publications discussing properties of this approach the motion mechanism of quantum particles was just briefly studied, and there is still some research to do. This paper presents the results of our research on this subject. The novelty is based on a new type of distributions of particles in a quantum cloud. Obtained results allow to derive some guidelines of an effective tuning of the mechanism of distribution in the quantum cloud and show that further improvement of mSO is possible.
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
Abraham, A., Grosan, C., Ramos, V. (eds.): Stigmergic Optimization. Studies in Computational Intelligence, vol. 31. Springer, Heidelberg (2006)
Blackwell, T.: Particle Swarm Optimization in Dynamic Environments. In: Evolutionary Computation in Dynamic and Uncertain Environments. Studies in Computational Intelligence, vol. 51, pp. 29–49. Springer, Heidelberg (2007)
Blackwell, T., 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)
Blackwell, T., Branke, J.: Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation 10(4), 459–472 (2006)
Bohachevsky, I.O., Johnson, M.E., Stein, M.L.: Generalized simulated annealing for function optimization. Technometrics 28(3), 209–217 (1986)
Branke, J.: Memory enhanced evolutionary algorithm for changing optimization problems. In: Proc. of the Congress on Evolutionary Computation, vol. 3, pp. 1875–1882. IEEE Press, Piscataway (1999)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Dordrecht (2002)
Brooks, D.G., Verdini, W.A.: Computational experience with generalized simulated annealing over continuous variables. Am. J. Math. Manage. Sci. 8(3-4), 425–449 (1988)
Chambers, J.M., Mallows, C.L., Stuck, B.W.: A method for simulating stable random variables. J. Amer. Statist. Assoc. 71(354), 340–344 (1976)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multi-dimensional complex space. IEEE Trans. on Evolutionary Computation 6(1), 58–73 (2002)
Eberhart, R.C., Kenendy, J.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)
Li, X.: Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 105–116. Springer, Heidelberg (2004)
Li, X., Branke, J., Blackwell, T.: Particle swarm with speciation and adaptation in a dynamic environment. In: Keijzer, M., et al. (eds.) GECCO 2006: Proc. Conference on Genetic and Evolutionary Computation, pp. 51–58. ACM Press, New York (2006)
Morrison, R.W., De Jong, K.A.: A test problem generator for non-stationary environments. In: Proc. of the Congress on Evaluationary Computation, vol. 3, pp. 1859–1866. IEEE Press, Piscataway, NJ (1999)
Obuchowicz, A., Pretki, P.: Phenotypic evolution with a mutation based on symmetric α stable distributions. Int. J. Appl. Math. Comput. Sci. 14(3), 289–316 (2004)
Obuchowicz, A., Pretki, P.: Isotropic symmetric α-stable mutations for evolutionary algorithms. In: Corne, D., et al. (eds.) The 2005 IEEE Congress on Evolutionary Computation, pp. 404–410. IEEE Press, Los Alamitos (2005)
Parrot, D., Li, X.: Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans. Evol. Comput. 10(4), 440–458 (2006)
Romeijn, H.E., Smith, R.L.: Simulated annealing for constrained global optimization. J. Global Optim. 5(2), 101–126 (1994)
Trojanowski, K.: B-cell algorithm as a parallel approach to optimization of moving peaks benchmark tasks. In: Saeed, K., Abraham, A., Mosdorf, R. (eds.) Sixth International Conference on Computer Information Systems and Industrial Management Applications (CISIM 2007), Poland, June 28-30, pp. 143–148. IEEE Computer Society Conference Publishing Services (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Trojanowski, K. (2008). Tuning Quantum Multi-Swarm Optimization for Dynamic Tasks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_49
Download citation
DOI: https://doi.org/10.1007/978-3-540-69731-2_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69572-1
Online ISBN: 978-3-540-69731-2
eBook Packages: Computer ScienceComputer Science (R0)