Abstract
This work explores the utilization of the island-model within the context of Particle Swarm Optimization (PSO). The well-known notions of decentralized evolutionary algorithms are extended to this context, resulting in the definition of a multi-swarm. The influence that different parameterizations of the model, namely, the number of swarms, their interconnection topology, the policy for selecting particles to migrate, and the policy for accepting incoming particles is studied. Four continuous optimization problems are used for this purpose. The experimental results indicate that a moderate number of swarms arranged in a fully-connected topology provide the best results.
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 the Fourth IEEE International Conference on Neural Networks, Piscataway NJ, IEEE Press (1995) 1942–1948
Kennedy, J., Eberhart, R.: The particle swarm: Social adaptation in information processing systems. In Corne, D., Dorigo, M., Glover, F., eds.: New Ideas in Optimization. McGraw-Hill IK (1999) 379–387
Eberhart, R., Shi, Y.: Particle swarm optimization: development, applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, Piscataway NJ, IEEE Press (2001) 81–86
Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco CA (2001)
Riget, J., Vesterstrøm, J.: A diversity-guided particle swarm optimizer — the ARPSO. Technical Report 2002-02, EVALife Project Group, Department of Computer Science, Universit of Aahrus (2002)
Krink, T., Løvbjerg, M.: The lifecycle model: Combining particle swarm optimisation, genetic algorithms and hillclimbers. In Merelo, J., Adamidis, P., Beyer, H.G., Fernández-Villacañas, J.L., Schwefel, H.P., eds.: Parallel Problem Solving From Nature VII. Volume 2439 of Lecture Notes in Computer Science., Berlin, Springer-Verlag (2002) 621–630
Angeline, P.: Using selection to improve particle swarm optimization. In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, IEEE Press (1998) 84–89
Tanese, R.: Distributed genetic algorithms. In Schaffer, J., ed.: Proceedings of the Third International Conference on Genetic Algorithms, San Mateo, CA, Morgan Kaufmann Publishers (1989) 434–439
Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Transactions on Evolutionary Computation 6 (2002) 443–462
Moscato, P., Cotta, C.: A gentle introduction to memetic algorithms. In Glover, F., Kochenberger, G., eds.: Handbook of Metaheuristics. Kluwer Academic Publishers, Boston MA (2003) 105–144
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Romero, J.F., Cotta, C. (2005). Optimization by Island-Structured Decentralized Particle Swarms. In: Reusch, B. (eds) Computational Intelligence, Theory and Applications. Advances in Soft Computing, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31182-3_3
Download citation
DOI: https://doi.org/10.1007/3-540-31182-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22807-3
Online ISBN: 978-3-540-31182-9
eBook Packages: EngineeringEngineering (R0)