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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Brutschy, A., Scheidler, A., Merkle, D., & Middendorf, M. (2008). Learning from House-Hunting Ants: Collective Decision-Making in Organic Computing Systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5217 LNCS, pp. 96–107). https://doi.org/10.1007/978-3-540-87527-7_9
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