NeuralGLS: learning to guide local search with graph convolutional network for the traveling salesman problem-Reference-Cited by-同舟云学术

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NeuralGLS: learning to guide local search with graph convolutional network for the traveling salesman problem

Published:2023-10-10 Issue: Volume: Page:
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Sui Jingyan,Ding Shizhe,Xia Boyang,Liu Ruizhi,Bu Dongbo

Funder

National Key Research and Development Program of China

ComputeX center, ICT, CAS

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-023-09042-6.pdf

Reference39 articles.

1. La Maire BFJ, Mladenov VM (2012) Comparison of neural networks for solving the travelling salesman problem. In: 11th symposium on neural network applications in electrical engineering, pp. 21–24. https://doi.org/10.1109/NEUREL.2012.6419953

2. Laporte G (1992) The traveling salesman problem: An overview of exact and approximate algorithms. Eur J Operat Res 59(2):231–247. https://doi.org/10.1016/0377-2217(92)90138-Y

3. Matai R, Singh S, Mittal ML (2010) Traveling salesman problem: an overview of applications, formulations, and solution approaches. In: Davendra D (ed) Traveling salesman problem. IntechOpen, Rijeka. Chap. 1. https://doi.org/10.5772/12909

4. Bengio Y, Lodi A, Prouvost A (2021) Machine learning for combinatorial optimization: a methodological tour d’horizon. Eur J Operat Res 290(2):405–421. https://doi.org/10.1016/j.ejor.2020.07.063

5. Vinyals O, Fortunato M, Jaitly N (2015) Pointer networks. Adv Neural Inf Process Syst 28:56248

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