Abstract
In this paper, we propose an ant colony optimization based on the predicted traffic for time-dependent traveling salesman problems (TDTSP), where the travel time between cities changes with time. Prediction values required for searching is assumed to be given in advance. We previously proposed a method to improve the search rate of Max-Min Ant System (MMAS) for static TSPs. In the current work, the method is extended so that the predicted travel time can be handled and formalized in detail. We also present a method of generating a TDTSP to use in evaluating the proposed method. Experimental results using benchmark problems with 51 to 318 cities suggested that the proposed method is better than the conventional MMAS in the rate of search.
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Kanoh, H., Ochiai, J. (2012). Solving Time-Dependent Traveling Salesman Problems Using Ant Colony Optimization Based on Predicted Traffic. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., RodrÃguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_4
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DOI: https://doi.org/10.1007/978-3-642-28765-7_4
Publisher Name: Springer, Berlin, Heidelberg
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