期刊
APPLIED SOFT COMPUTING
卷 71, 期 -, 页码 268-276出版社
ELSEVIER
DOI: 10.1016/j.asoc.2018.07.010
关键词
Travelling salesman problem; Difficulty evaluation; Dirichlet tessellation; Delaunay triangulation; Weibull probability distribution; Genetic algorithm
资金
- Spanish Ministry of Economy and Competitiveness (MINECO) [FPA2013-47804-C2-1-R, FPA2016-80994-C2-1-R]
- Unidad de Excelencia Maria de Maeztu: CIEMAT - FISICA DE PARTICULAS [MDM-2015-0509]
Travelling salesman problem is a classical combinatorial optimization problem. Instances of this problem have been used as benchmark for testing the performance of many proposals of discrete optimizer algorithms. However, the hardness or the difficulty degree to solve the instances has not been usually addressed. In the past the evaluation of the difficulty of the instances has required to obtain a high-quality solution, ideally the optimal one. However, this type of strategy burdens the evaluation with large processing times. In this work, diverse indirect measures for evaluating the hardness to solve instances of the travelling salesman problem are proposed. These evaluations are inferred from the spatial attributes of previously evaluated instances, and later correlated with the hardness of the instances. Finally, where a significant correlation is found, a linear model is built and linked to a genetic algorithm. As a consequence of this work, mechanisms for hardening instances of travelling salesman problem are implemented. (C) 2018 Published by Elsevier B.V.
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