Advertisement

Multi-type Ant Colony: The Edge Disjoint Paths Problem

  • Ann Nowé
  • Katja Verbeeck
  • Peter Vrancx
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3172)

Abstract

In this paper we propose the Multi-type Ant Colony system, which is an extension of the well known Ant System. Unlike the Ant System the ants are of a predefined type. In the Multi-type Ant Colony System ants have the same goal as their fellow types ants, however are in competition with the ants of different types. The collaborative behavior between identical type ants is modeled the same way as in ant systems, i.e. ants are attracted to pheromone of ants of the same type. The competition between different types is obtained because ants are repulsed by the pheromone of ants of other types. This paradigm is interesting for applications where collaboration as well as competition is needed in order to obtain good solutions. In this paper we illustrate the algorithm on the problem of finding disjoint paths in graphs. A first experiment shows on a simple graph two ants types that find successfully two completely disjoint paths. A second experiment shows a more complex graph where the number of required disjoint paths exceeds the number of possible disjoint paths. The results show that the paths found by the ants are distributed proportionally to the cost of the paths. A last experiment shows the influence of the exploration parameter on a complex graph. Good results are obtained if the exploration parameter is gradually decreased.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blesa, M., 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.
    Caro, G.D., Antnet, M.D.: Stigmergetic control for communications network. Journal in Artificial Intelligence Research 9, 317–365 (1998)zbMATHGoogle Scholar
  3. 3.
    Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation (1997)Google Scholar
  4. 4.
    Van Dycke Parunak, H., Brueckner, S., Missionaries, A.-L., Cannibals: Synthetic Pheromones for distributed motion control. In: Proceedings of the Fourth International Conference on Autonomous Agents (Agents 2000), Barcelona, Spain (2000)Google Scholar
  5. 5.
    Van Dycke Parunak, H., Brueckner, S., Sauter, J.: Synthetic Pheromone Mechanisms for Coordination of Unmanned Vehicles. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems (2002)Google Scholar
  6. 6.
    Theraulaz, G., Bonabeau, E., Dorigo, M.: Swarm Intelligence, From Natural to Artificial Systems. In: Santa Fe Institute studies in the sciences of complexity, Oxford University Press, Oxford (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ann Nowé
    • 1
  • Katja Verbeeck
    • 1
  • Peter Vrancx
    • 1
  1. 1.Vrije Universiteit Brussel, COMOBrusselBelgium

Personalised recommendations