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.
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References
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)
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)
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)
Di Caro, G., Dorigo, M.: Antnet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research 9(1), 317–365 (1998)
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)
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)
Maier, M.: On architecting and intelligent transport systems. IEEE Transactions on Aerospace and Electronic Systems 33(2), 610–625 (1997)
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)
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)
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© 2012 Springer-Verlag Berlin Heidelberg
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Claes, R., Holvoet, T. (2012). Cooperative Ant Colony Optimization in Traffic Route Calculations. In: Demazeau, Y., Müller, J., Rodríguez, J., Pérez, J. (eds) Advances on Practical Applications of Agents and Multi-Agent Systems. Advances in Intelligent and Soft Computing, vol 155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28786-2_3
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DOI: https://doi.org/10.1007/978-3-642-28786-2_3
Publisher Name: Springer, Berlin, Heidelberg
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