Abstract
This chapter presents an algorithm, the flock of starlings optimization that is inspired both to the famous Particle Swarm Optimization (PSO) and to recent naturalistic observations on collective animal behaviour, performed by M. Ballerini et al. The presented algorithm implements a virtual flock governed by topological interactions between its members. The proposed approach has been validated by using classical benchmarks and compared with different versions of PSO. Results have shown that the algorithm has high exploration capability, avoids local minima entrapments and is particularly suitable for multimodal optimizations.
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.
Similar content being viewed by others
References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, vol. IV, pp. 1942–1948 (1995)
Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. Computer Graphics 21(4), 25–34 (1987)
Heppner, F., Grenander, U.: A stochastic nonlinear model for coordinated bird flocks. In: Krasner, S. (ed.) The Ubiquity of Chaos. AAAS Publications, Washington (1990)
Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Lecomte, V., Orlandi, A., Parisi, G., Procaccini, M., Viale, M., Zdravkovic, V.: Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proceedings of the National Academy of Science, 1232–1237 (2008)
Engelbrecht, A.P.: Computational Intelligence: An Introduction. Wiley, Chichester (2002)
Montes de Oca, M.A., Stutzle, T., Birattari, M., Dorigo, M.: Frankenstein’s PSO: a composite particle swarm optimization algorithm. IEEE Transactions on Evolutionary Computation 13, 1120–1132 (2009)
Clerc, M., Kennedy, J.: The particle swarm: explosion stability and convergence in a multi-dimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Riganti Fulginei, F., Salvini, A.: Hysteresis model identification by the Flock-of-Starlings Optimization. International Journal of Applied Electromagnetics and Mechanics 30(3-4), 1383–5416 (2009)
Ali, M.M., Kaelo, P.: Improved particle swarm algorithms for global optimization. Applied Mathematics and Computation, pp. 578–593. Elsevier, Amsterdam (2008)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 67–83 (1997)
Riganti Fulginei, F., Salvini, A.: Comparative Analysis between Modern Heuristics and Hybrid Algorithms. COMPEL 26(2), 264–273 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Fulginei, F.R., Salvini, A. (2010). The Flock of Starlings Optimization: Influence of Topological Rules on the Collective Behavior of Swarm Intelligence. In: Wiak, S., Napieralska-Juszczak, E. (eds) Computational Methods for the Innovative Design of Electrical Devices. Studies in Computational Intelligence, vol 327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16225-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-16225-1_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16224-4
Online ISBN: 978-3-642-16225-1
eBook Packages: EngineeringEngineering (R0)