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

  • Arne Brutschy
  • Alexander Scheidler
  • Daniel Merkle
  • Martin Middendorf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5217)


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.


Nest Site Task Allocation Work Unit Supplementary Online Material Adaptive Model 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Arne Brutschy
    • 1
  • Alexander Scheidler
    • 2
  • Daniel Merkle
    • 3
  • Martin Middendorf
    • 2
  1. 1.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium
  2. 2.Parallel Computing and Complex Systems Group, Computer Science DepartmentUniversity of LeipzigLeipzigGermany
  3. 3.Department of Mathematics and Computer ScienceUniversity of Southern DenmarkOdenseDenmark

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