Abstract
Traveling salesman problem (TSP) is a well-known NP-hard combinatorial optimization problem. It has been solved by a number of exact and approximate algorithms and serves as a testbed for new heuristic and metaheuristic optimization algorithms. However, it is often not easy to evaluate the hardness (complexity) of a TSP instance. Simple measures such as the number of cities or the minimum (maximum) route length do not capture the internal structure of a TSP instance sufficiently. In this work, we propose a new method for the assessment of TSP instance complexity based on clustering. The new approach is evaluated on a set of randomized TSP instances with different structure and its relation to the performance of a selected metaheuristic TSP solver is studied.
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Acknowledgement
This work was supported by the Czech Science Foundation under the grant no. GJ16-25694Y and by the projects SP2017/100 “Parallel processing of Big Data IV” and SP2017/85 “Processing and advanced analysis of bio-medical data II,” of the Student Grant System, VŠB-Technical University of Ostrava.
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Krömer, P., Platoš, J. (2018). Evaluation of Traveling Salesman Problem Instance Hardness by Clustering. In: Krömer, P., Alba, E., Pan, JS., Snášel, V. (eds) Proceedings of the Fourth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2017. Advances in Intelligent Systems and Computing, vol 682. Springer, Cham. https://doi.org/10.1007/978-3-319-68527-4_41
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DOI: https://doi.org/10.1007/978-3-319-68527-4_41
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