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Cooperative Ant Colony Optimization in Traffic Route Calculations

  • Rutger Claes
  • Tom Holvoet
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 155)

Abstract

Ant Colony Optimization (ACO) algorithms tend to be isolated processes. When applying ACO principles to traffic route calculations, ants exploring the traffic network on behalf of a vehicle typically only perceive and apply pheromones related to that vehicle. Between ants exploring on behalf of different vehicles little cooperation exists. While such cooperation could improve the performance of the ACO algorithm, it is difficult to achieve because ants working on behalf of different vehicles are solving different problems. This paper presents and evaluates a method of cooperation between ants finding routes on behalf of different vehicles by sharing more general knowledge through pheromones. A simulation of the proposed approach is used to evaluate the cooperative ACO algorithm and to compare it with an uncooperative version based on the quality of the calculated routes and the number of iterations needed to find good results. The evaluation indicates that the quality of the solution does not improve and that the speedup is insignificant when using the collaborative variant.

Keywords

Outgoing Edge Link Travel Time Route Guidance Network Layout Virtual Time 
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.

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References

  1. 1.
    Ando, Y., Masutani, O., Sasaki, H., Iwasaki, H., Fukazawa, Y., Honiden, S.: Pheromone model: Application to traffic congestion prediction. Engineering Self-Organising Systems, 182–196 (2006)Google Scholar
  2. 2.
    Claes, R., Holvoet, T.: Ad hoc link traversal time predictions. In: Proceedings of the 14th International IEEE conference on Intelligent Transportation Systems, pp. 1803–1808 (2011)Google Scholar
  3. 3.
    Claes, R., Holvoet, T.: Ant colony optimization applied to route planning using link travel time predictions. In: 2011 IEEE International Symposium on Parallel & Distributed Processing Workshops, pp. 358–365 (2011)Google Scholar
  4. 4.
    Di Caro, G., Dorigo, M.: Antnet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research 9(1), 317–365 (1998)zbMATHGoogle Scholar
  5. 5.
    Dorigo, M., Gambardella, L.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (2002)CrossRefGoogle Scholar
  6. 6.
    Fujimoto, R., Hunter, M., Sirichoke, J., Palekar, M., Kim, H., Suh, W.: Ad hoc distributed simulations. In: PADS 2007: Proceedings of the 21st International Workshop on Principles of Advanced and Distributed Simulation, pp. 15–24 (2007)Google Scholar
  7. 7.
    Maier, M.: On architecting and intelligent transport systems. IEEE Transactions on Aerospace and Electronic Systems 33(2), 610–625 (1997)CrossRefGoogle Scholar
  8. 8.
    Tatomir, B., Rothkrantz, L.J., Suson, A.C.: Travel time prediction for dynamic routing using ant based control. In: Proceedings of the 2009 Winter Simulation Conference, pp. 1069–1078 (2009)Google Scholar
  9. 9.
    Wunderlich, K., Kaufman, D., Smith, R.: Link travel time prediction for decentralized route guidancearchitectures. IEEE Transactions on Intelligent Transportation Systems 1(1), 4–14 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceKU LeuvenLeuvenBelgium

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