The ML-Constructive heuristic is a recently presented method and the first hybrid method capable of scaling up to real scale traveling salesman problems. It combines machine learning techniques and classic optimization techniques. In this paper we present improvements to the computational weight of the original deep learning model. In addition, as simpler models reduce the execution time, the possibility of adding a local-search phase is explored to further improve performance. Experimental results corroborate the quality of the proposed improvements.

Machine Learning Constructives and Local Searches for the Travelling Salesman Problem / Vitali, T; Mele, U; Gambardella, Lm; Montemanni, R. - (2022), pp. 59-65. (Intervento presentato al convegno Operations Research 2021 tenutosi a Online nel 31.08.21-03.09.21) [10.1007/978-3-031-08623-6_10].

Machine Learning Constructives and Local Searches for the Travelling Salesman Problem

Montemanni, R
2022

Abstract

The ML-Constructive heuristic is a recently presented method and the first hybrid method capable of scaling up to real scale traveling salesman problems. It combines machine learning techniques and classic optimization techniques. In this paper we present improvements to the computational weight of the original deep learning model. In addition, as simpler models reduce the execution time, the possibility of adding a local-search phase is explored to further improve performance. Experimental results corroborate the quality of the proposed improvements.
2022
Operations Research 2021
Online
31.08.21-03.09.21
59
65
Vitali, T; Mele, U; Gambardella, Lm; Montemanni, R
Machine Learning Constructives and Local Searches for the Travelling Salesman Problem / Vitali, T; Mele, U; Gambardella, Lm; Montemanni, R. - (2022), pp. 59-65. (Intervento presentato al convegno Operations Research 2021 tenutosi a Online nel 31.08.21-03.09.21) [10.1007/978-3-031-08623-6_10].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1291424
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