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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Veröffentlicht in:	Expert systems with applications Jg. 187; S. 115948
Hauptverfasser:	Huerta, Isaías I., Neira, Daniel A., Ortega, Daniel A., Varas, Vicente, Godoy, Julio, Asín-Achá, Roberto
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 01.01.2022 Elsevier BV
Schlagworte:	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-Zugang:	Volltext
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