Abstract
The Team Orienteering Problem (TOP) is a generalization of the Orienteering Problem (OP). A limited number of vehicles is available to visit customers from a potential set. Each vehicle has a predefined running-time limit, and each customer has a fixed associated profit. The aim of the TOP is to maximize the total collected profit. In this paper we propose a simple hybrid Genetic Algorithm (GA) using new algorithms dedicated to the specific scope of the TOP: an Optimal Split procedure for chromosome evaluation and Local Search techniques for mutation. We have called this hybrid method a Memetic Algorithm (MA) for the TOP. Computational experiments conducted on standard benchmark instances clearly show our method to be highly competitive with existing ones, yielding new improved solutions in at least 11 instances.
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Bouly, H., Dang, DC., Moukrim, A. (2008). A Memetic Algorithm for the Team Orienteering Problem. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_71
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DOI: https://doi.org/10.1007/978-3-540-78761-7_71
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