Abstract
The particle swarm algorithm [1] contains elements which map fairly strongly to the group-foraging problem in behavioural ecology: its continuous equations of motion include concepts of social attraction and communication between individuals, two of the general requirements for grouping behaviour [2]. Despite its socio-biological background, the particle swarm algorithm has rarely been applied to biological problems, largely remaining a technique used in classical optimisation problems. In this paper [3], we show how some simple adaptions to the standard algorithm can make it well suited for the foraging problem.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
Krause, J., Ruxton, G.D.: Living in Groups. Oxford University Press, Oxford (2002)
Di Chio, C., Poli, R., Di Chio, P.: Extending the Particle Swarm Algorithm to Model Animal Foraging Behaviour. University of Essex (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Di Chio, C., Poli, R., Di Chio, P. (2006). Extending the Particle Swarm Algorithm to Model Animal Foraging Behaviour. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2006. Lecture Notes in Computer Science, vol 4150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11839088_58
Download citation
DOI: https://doi.org/10.1007/11839088_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38482-3
Online ISBN: 978-3-540-38483-0
eBook Packages: Computer ScienceComputer Science (R0)