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.
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References
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)
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© 2006 Springer-Verlag Berlin Heidelberg
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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
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DOI: https://doi.org/10.1007/11839088_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38482-3
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