Abstract
This paper proposes ant-inspired strategies for self-organized and decentralized collective decision-making in computing systems which employ reconfigurable units. The particular principles used for the design of these strategies are inspired by the house-hunting of the ant Temnothorax albipennis. The considered computing system consists of two types of units: so-called worker units that are able to execute jobs that come into the system, and scout units that are additionally responsible for the reconfiguration process of all units. The ant-inspired strategies are analyzed experimentally and are compared to a non-adaptive reference strategy. It is shown that the ant-inspired strategies lead to a collective decentralized decision process through which the units are able to find good configurations that lead to a high system throughput even in complex configuration spaces.
Supplementary online material: http://iridia.ulb.ac.be/supp/IridiaSupp2008-006/
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Brutschy, A., Scheidler, A., Merkle, D., Middendorf, M. (2008). Learning from House-Hunting Ants: Collective Decision-Making in Organic Computing Systems. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2008. Lecture Notes in Computer Science, vol 5217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87527-7_9
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DOI: https://doi.org/10.1007/978-3-540-87527-7_9
Publisher Name: Springer, Berlin, Heidelberg
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