The neural network methods for solving Traveling Salesman Problem

被引：14
|
作者：
Shi, Yong [1 ,2 ,3 ,4 ]
Zhang, Yuanying [2 ,3 ,5 ]
机构：
[1] Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
[2] Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China
[3] Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China
[4] Univ Nebraska Omaha, Coll Informat Sci & Technol, Omaha, NE 68182 USA
[5] Univ Chinese Acad Sci, Sch Math Sci, Beijing 100190, Peoples R China
来源：
8TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT (ITQM 2020 & 2021): DEVELOPING GLOBAL DIGITAL ECONOMY AFTER COVID-19 | 2022年 / 199卷
基金：
中国国家自然科学基金;
关键词：
Traveling Salesman Problem; Neural network; Hopfield neural network; Graph neural network; Reinforcement learning; SIMULATED ANNEALING ALGORITHM; OPTIMIZATION;
D O I：
10.1016/j.procs.2022.01.084
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Traveling Salesman Problem(TSP) is a main attention issue at present. Neural network can be used to solve combinatorial optimization problems. In recent years, there have existed many neural network methods for solving TSP, which has made a big step forward for solving combinatorial optimization problems. This paper reviews the neural network methods for solving TSP in recent years, including Hopfield neural network, graph neural network and neural network with reinforcement learning. Using neural network to solve TSP can effectively improve the accuracy of the approximate solution. Finally, we put forward the prospect of solving TSP in the future. (C) 2021 The Authors. Published by Elsevier B.V.
引用
收藏
页码：681 / 686
页数：6
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