Abstract
This paper introduces a method to minimize distributed PSO algorithm execution time in a grid computer environment, based on a reduction in the information interchanged among the demes involved in the process of finding the best global fitness solution. Demes usually interchange the best global fitness solution they found at each iteration. Instead of this, we propose to interchange information only after an specified number of iterations are concluded. By applying this technique, it is possible to get a very significant execution time decrease without any loss of solution quality.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bratton, D., Kennedy, J.: Defining a Standard for Particle Swarm Optimization. In: Proceedings of the 2007 IEEE Swarm Intelligence Symposium (SIS 2007) (2007)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948. IEEE Service Center, Pistcataway (1995)
Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann Publisher, San Francisco (2001)
Kennedy, J.: Stereotyping: improving particle swarm performance with cluster analysis. In: Proceedins of the IEEE International Conference on Evolutionary Computation, pp. 1507–1512 (2000)
Kennedy, J., Mendes, R.: Neighborhood topologies in fully informed and best-of-neighbothood particle swarms. IEE Transations on Systems, Man and Cybernetics, Part C: Applications and Reviews 36(4), 515–519 (2006)
Liu, D.S., Tan, K.C., Ho, W.K.: A Distributed Co-evolutionary Particle Swarm Optimization Algorithm. In: 2007 IEEE Congress on Evolutionary Computation (CEC 2007) (2007)
Guha, T., Ludwig, S.A.: Comparison of Service Selection Algorithms for Grid Services: Multiple Objetive Particle Swarm Optimization and Constraint Satisfaction Based Service Selection. In: Proceedings - International Conference on Tools with Artificial Intelligence (ICTAI 1, art. no. 4669686), pp. 172–179 (2008)
Jiao, B., Lian, Z., Gu, X.: A dinamic inertia weight particle swarm optimization algorithm. Chaos, Solitons and Fractals 37, 698–705 (2008)
Scriven, I., Lewis, A., Ireland, D., Junwei, L.: Decentralised Distributed Multiple Objective Particle Swarm Optimisation Using Peer to Peer Networks. In: IEEE Congress on Evolutionary Computation, CEC 2008, art. no. 4631191, pp. 2925–2928 (2008)
Burak Atat, S., Gazi, V.: Decentralized Asynchronous Particle Swarm Optimization. In: IEEE Swarm Intelligence Symposium, SIS 2008, art. no. 4668304 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Parra, F., Galan, S.G., Yuste, A.J., Prado, R.P., Muñoz, J.E. (2009). A Method to Minimize Distributed PSO Algorithm Execution Time in Grid Computer Environment. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds) Bioinspired Applications in Artificial and Natural Computation. IWINAC 2009. Lecture Notes in Computer Science, vol 5602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02267-8_51
Download citation
DOI: https://doi.org/10.1007/978-3-642-02267-8_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02266-1
Online ISBN: 978-3-642-02267-8
eBook Packages: Computer ScienceComputer Science (R0)