Multi-type ACO for Light Path Protection

  • Peter Vrancx
  • Ann Nowé
  • Kris Steenhaut
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3898)


Backup trees (BTs) are a promising approach to network protection in optical networks. BTs allow us to protect a group of working paths against single network failures, while reserving only a minimum amount of network capacity for backup purposes. The process of constructing a set of working paths together with a backup tree is computationally very expensive, however. In this paper we propose a multi-agent approach based on ant colony optimization (ACO) for solving this problem. ACO algorithms use a set of relatively simple agents that model the behavior of real ants. In our algorithm multiple types of ants are used. Ants of the same type collaborate, but are in competition with the ants of other types. The idea is to let each type find a path in the network that is disjoint with that of other types. We also demonstrate a preliminary version of this algorithm in a series of simple experiments.


Source Node Destination Node Optical Network Disjoint Path Backup Path 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blesa, M.J., Blum, C.: Ant colony optimization for the maximum edge-disjoint paths problem. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 160–169. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence, From Natural to Artificial Systems. Santa Fe Institute studies in the sciences of complexity. Oxford University Press, Oxford (1999)MATHGoogle Scholar
  3. 3.
    Caro, G.D., Dorigo, M., Gambardella, L.M.: Antnet: A mobile agents approach to adaptive routing. Technical report, IRIDIA, Université Libre de Bruxelles Brussels, Belgium (1997)Google Scholar
  4. 4.
    Colorni, A., Dorigo, M., Maffioli, F., Maniezzo, V., Righini, G., Trubian, M.: Heuristics from nature for hard combinatorial optimization problems. International Transactions in Operational Research (1996), Multi-type ACO for Light Path Protection 215Google Scholar
  5. 5.
    Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5, 137–172 (1999)CrossRefGoogle Scholar
  6. 6.
    Dorigo, M., Caro, G.D.: The ant colony optimization meta-heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas In Optimization. McGraw-Hill, Maidenhaid (1999)Google Scholar
  7. 7.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: Optimization by a colony of cooperating agents. IEE Transactions on Systems, Man, and Cybernetics (1996)Google Scholar
  8. 8.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)MATHGoogle Scholar
  9. 9.
    Groebbens, A., Tran, L., Colle, D., Steenhaut, K., Maesschalck, S.D., Nowe, A., Lievens, I., Pickavet, M., Demeester, P.: Efficient protection in mplambdas networks using backup trees: Part1- concepts and heuristics. Photonic Network Communications 6(3), 207–222 (2003)CrossRefGoogle Scholar
  10. 10.
    Groebbens, A., Tran, L., Colle, D., Steenhaut, K., Maesschalck, S.D., Nowe, A., Lievens, I., Pickavet, M., Demeester, P.: Efficient protection in mplambdas networks using backup trees: Part2- simulations. Photonic Network Communications 6(3), 191–206 (2003)CrossRefGoogle Scholar
  11. 11.
    Kawamura, H., Yamamoto, M., Suzuki, K., Ohuchi, A.: Multiple ant colonies algorithm based on colony level interactions. IEICE Transactions on Fundamentals E83-A(2), 371–379 (2000)Google Scholar
  12. 12.
    Nowé, A., Verbeeck, K., Vrancx, P.: Multi-type ant colony: the edgedisjoint paths problem. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 202–213. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Subramanian, D., Druschel, P., Chen, P.: Ants and reinforcement learning: A case study in routing in dynamic networks. In: Proceedings of IJCAI 1997, International Joint Conference on Artificial Intelligence, pp. 832–838 (1997)Google Scholar
  15. 15.
    Navarro Varela, G., Sinclair, M.C.: Ant colony optimisation for virtualwavelength- path routing and wavelength allocation. In: Proceedings of the Congress on Evolutionary Computation, CEC 1999 (1999)Google Scholar
  16. 16.
    Vrancx, P.: Multi-type ant system: Introducing competition to ant algorithms. Master’s thesis, Vrije Universiteit Brussel (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter Vrancx
    • 1
  • Ann Nowé
    • 1
  • Kris Steenhaut
    • 1
  1. 1.Vrije Universiteit BrusselBelgium

Personalised recommendations