Improving the state-of-the-art in the Traveling Salesman Problem: An Anytime Automatic Algorithm Selection

This work presents a new metaheuristic for the euclidean Traveling Salesman Problem (TSP) based on an Anytime Automatic Algorithm Selection model using a portfolio of five state-of-the-art solvers. We introduce a new spatial representation of nodes, in the form of a matrix grid, avoiding costly calc...

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Bibliographic Details
Published in:Expert systems with applications Vol. 187; p. 115948
Main Authors: Huerta, Isaías I., Neira, Daniel A., Ortega, Daniel A., Varas, Vicente, Godoy, Julio, Asín-Achá, Roberto
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.01.2022
Elsevier BV
Subjects:
Algorithm selection problem
Algorithms
Anytime behavior approach
Artificial neural networks
Combinatorial optimization
Datasets
Heuristic methods
Machine learning
Ranking
Representations
Solvers
Traveling salesman problem
TSP
TSP
Anytime behavior approach
Combinatorial optimization
Algorithm selection problem
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:This work presents a new metaheuristic for the euclidean Traveling Salesman Problem (TSP) based on an Anytime Automatic Algorithm Selection model using a portfolio of five state-of-the-art solvers. We introduce a new spatial representation of nodes, in the form of a matrix grid, avoiding costly calculation of features. Furthermore, we use a new compact staggered representation for the ranking of algorithms at each time step. Then, we feed inputs (matrix grid) and outputs (staggered representation) into a classifying convolutional neural network to predict the ranking of the solvers at a given time. We use the available datasets for TSP and generate new instances to augment their number, reaching 6,689 instances, distributed into training and test sets. Results show that the time required to predict the best solver is drastically reduced in comparison to previous traditional feature selection and machine learning methods. Furthermore, the prediction can be obtained at any time and, on average, the metasolver is better than running all the solvers separately on all the datasets, obtaining 79.8% accuracy. •A new metaheuristic for the TSP based on Automatic Algorithm Selection is proposed.•We consider the anytime nature of the solution computation by the solvers in our work.•A spatial representation of instances is presented, avoiding calculation of features.•Our approach outperforms state-of-the-art solvers in public data sets.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115948