Abstract
The algorithms belonging to the Ant Colony Optimization metaheuristic can be naturally applied to shortest path related problems. Although not so intensively studied as TSP, the multiple traveling salesman problem (multiple-TSP) is a straightforward extension of TSP of practical importance, requiring more than one salesman to be used for covering the whole set of cities. This work tackles the MinMax formulation of multiple-TSP, which aims at obtaining balanced subtours for the salesmen. An Ant Colony System that follows the MinMax formulation is proposed. Two other algorithms combining K-Means and Fuzzy C-Means clustering with Ant Colony Systems are described. The experimental analysis investigates the performance of the proposed algorithms from a bi-objective perspective, counting for the total length/cost of a solution and its balancing degree measured as the amplitude of its subtours.
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Necula, R., Raschip, M., Breaban, M. (2018). Balancing the Subtours for Multiple TSP Approached with ACS: Clustering-Based Approaches Vs. MinMax Formulation. In: Tantar, AA., Tantar, E., Emmerich, M., Legrand, P., Alboaie, L., Luchian, H. (eds) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI. Advances in Intelligent Systems and Computing, vol 674. Springer, Cham. https://doi.org/10.1007/978-3-319-69710-9_15
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DOI: https://doi.org/10.1007/978-3-319-69710-9_15
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