Advertisement

Congestion Control in Ant Like Moving Agent Systems

  • Alexander Scheidler
  • Daniel Merkle
  • Martin Middendorf
Part of the IFIP – The International Federation for Information Processing book series (IFIPAICT, volume 268)

Abstract

In this paper we study the problem of congestion in system where agents move according to simple ant inspired movement rules. It is assumed that the agents have to visit a service station to refill their energy storage. After visiting the service station the ants can move randomly and fast. The less energy an agent has the slower it becomes and the more it moves in direction of the service station. Different methods for self-organized congestion control are proposed in this paper where the behavior of the agents compared to the original is not changed or is changed only slightly without the need to use any global information and without using additional sensory information. The proposed systems are investigated with

Keywords

Movement Model Service Station Service Area Congestion Control Internal Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    M. Dorigo, V. Maniezzo, A. Colorni: Positive feedback as a search strategy. Tech Rep., 91-016, Dip Elettronica, Politecnico di Milano, Italy, 1991.Google Scholar
  2. 2.
    . M. Dorigo, T. Stützle: Ant Colony Optimization. MIT Press, 2004.Google Scholar
  3. 3.
    . J.-L. Deneubourg, S. Goss, N. Franks, A.B. Sendova-Franks, C. Detrain, L. Chretien: The dynamics of collective sorting: Robot-like ants and ant-like robots. In Proc. of the 1st Int. Conf on Simulation of Adaptive Behavior, 356-363, 1991.Google Scholar
  4. 4.
    J. Handl, B. Meyer: Ant-based and swarm-based clustering. 1(2), 95-113, 2007Google Scholar
  5. 5.
    . H. Schmeck: Organic Computing - A New Vision for Distributed Embedded Systems. Proc. of the Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC 2005), 201-203, 2005.Google Scholar
  6. 6.
    . T. Schöler, C. Müller-Schloer: An Observer/Controller Architecture for Adaptive Reconfigurable Stacks. Proceedings ARCS 05, Springer, LNCS 3432, 139-153, 2006.Google Scholar
  7. 7.
    D. Merkle, M. Middendorf, A. Scheidler: Organic Computing and Swarm Intelligence In C. Blum, M. Merkle (Eds.), Swarm Intelligence, Springer, 2008.Google Scholar
  8. 8.
    J.L. Deneubourg, A. Lioni, C. Detrain: Dynamics of Aggregation and Emergence of Cooperation. Biol. Bull., 202:262-267, 2002.CrossRefGoogle Scholar
  9. 9.
    S. Depickère, D. Fresneau, J.-L. Deneubourg. A basis for spatial and social patterns in ant species: dynamics and mechanisms of aggregation. Journal of Insect Behavior, 17 (1): 81-97, 2004.CrossRefGoogle Scholar
  10. 10.
    R.V. Sole, E. Bonabeau, J. Delgado, P. Fernandez, J. Marin: Pattern formation and optimization in army ant raids. Artificial Life, 6(3):219-226, 2000.CrossRefGoogle Scholar
  11. 11.
    G. Theraulaz, E. Bonabeau, S.C. Nicolis, R.V. Sole, V. Fourcassie, S. Blanco, R. Fournier, J.L. Joly, P. Fernandez, A. Grimal, P. Dalle, J.L. Deneubourg: Spatial patterns in ant colonies. Proc. Natl. Acad. Sci., 99(15):9645-9649, 2002.zbMATHCrossRefGoogle Scholar
  12. 12.
    A.B. Sendova-Franks and J.V. Lent. Random walk models of worker sorting in ant colonies. Journal of Theoretical Biology, 217:255-274,2002.CrossRefMathSciNetGoogle Scholar
  13. 13.
    C. Melhuish, A. B. Sendova-Franks, S. Scholes, I. Horsfield, F. Welsby: Ant-inspired sorting by robots: the importance of initial clustering. J R Soc Interface. 3(7), 235-242, 2006.CrossRefGoogle Scholar
  14. 14.
    . A. Scheidler, D. Merkle, M. Middendorf: Emergent Sorting Patterns and Individual Differences of Randomly Moving Ant Like Agents. Proc. 7th German Workshop on Artificial Life (GWAL-7), IOS Press, 11 pp., 2006.Google Scholar
  15. 15.
    M.D. Cox, G.B. Blanchard: Gaseous templates in ant nests. Journal of Theoretic Biology, 204:223-238, 2000.CrossRefGoogle Scholar
  16. 16.
    G. Nicolas and D. Sillans: Immediate and latent effects of carbon dioxide on insects. Annual Review of Entomology, 34:97-116, 1989.CrossRefGoogle Scholar
  17. 17.
    D. Grünbaum, Schooling as a strategy for taxis in a noisy environment. Evolutionary Ecology, 12: 503-522, 1998.CrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2008

Authors and Affiliations

  • Alexander Scheidler
    • 1
  • Daniel Merkle
    • 2
  • Martin Middendorf
    • 1
  1. 1.Department of Computer ScienceUniversity of LeipzigLeipzigGermany
  2. 2.Departement of Mathematics & Computer ScienceUniversity of Southern DenmarkOdense MDenmark

Personalised recommendations