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

Arne Brutschy, Alexander Scheidler, Daniel Merkle, Martin Middendorf

Research output: Contribution to journalConference articleResearchpeer-review


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.
Original languageEnglish
Book seriesLecture Notes in Computer Science
Pages (from-to)96-107
Publication statusPublished - 2008
Event6th International Conference on Ant Colony Optimization and Swarm Intelligence (ANTS 2008) - Brussels, Belgium
Duration: 24. Aug 2010 → …


Conference6th International Conference on Ant Colony Optimization and Swarm Intelligence (ANTS 2008)
Period24/08/2010 → …


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