Abstract
This paper considers the use of randomly generated directed graphs as neighborhoods for particle swarm optimizers (PSO) using fully informed particles (FIPS), together with dynamic changes to the graph during an algorithm run as a diversity-preserving measure. Different graph sizes, constructed with a uniform out-degree were studied with regard to their effect on the performance of the PSO on optimization problems. Comparisons were made with a static random method, as well as with several canonical PSO and FIPS methods. The results indicate that under appropriate parameter settings, the use of random directed graphs with a probabilistic disruptive re-structuring of the graph produces the best results on the test functions considered.
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: Proc. of IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Press, Los Alamitos (1995)
Kennedy, J., Eberhart, R., Shi, Y.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)
Mendes, R.: Population Toplogies and Their Influence in Particle Swarm Performance. PhD thesis, Universidade do Minho, Braga, Portugal (2004)
Ashlock, D., Smucker, M., Walker, J.: Graph based genetic algorithms. In: Proc. of the IEEE Congress on Evolutionary Computation, pp. 1362–1368. IEEE Press, Los Alamitos (1999)
Kennedy, J.: Stereotyping: Improving particle swarm performance with cluster analysis. In: Proc. of the IEEE Congress on Evolutionary Computation, pp. 1507–1512 (2000)
Suganthan, P.N.: Particle swarm optimiser with neighbourhood operator. In: Proc. of the IEEE Congress on Evolutionary Computation, pp. 1958–1962. IEEE Press, Los Alamitos (1999)
Mohais, A., Ward, C., Posthoff, C.: Randomized directed neighborhoods with edge migration in particle swarm optimization. In: Proc. of the 2004 IEEE Congress on Evolutionary Computation, pp. 548–555. IEEE Press, Los Alamitos (2004)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6, 58–73 (2002)
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: Simple, maybe better. IEEE Transactions on Evolutionary Computation 8, 204–210 (2004)
Gouri, K., Bhattacharyya, R.A.J.: Statistical Concepts and Methods (May 1977)
Holm, S.: A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979)
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
Mohais, A.S., Mendes, R., Ward, C., Posthoff, C. (2005). Neighborhood Re-structuring in Particle Swarm Optimization. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_80
Download citation
DOI: https://doi.org/10.1007/11589990_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
eBook Packages: Computer ScienceComputer Science (R0)