Learning from House-Hunting Ants: Collective Decision-Making in Organic Computing Systems

Authors:
Arne Brutschy;Alexander Scheidler;Daniel Merkle;Martin Middendorf
Affiliations:
IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium;Parallel Computing and Complex Systems Group, Computer Science Department, University of Leipzig, Leipzig, Germany;Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark;Parallel Computing and Complex Systems Group, Computer Science Department, University of Leipzig, Leipzig, Germany
Venue:
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Year:
2008

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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.