Abstract
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.
1. Introduction
Traveling Salesman Problem(TSP) is a famous NP hard problem in combinatorial optimization [1]. And there is no algorithm that can find the optimal solution in polynomial time. The specific problem description is that a traveler wants to travel to ?? cities, and he is required to travel to each city only once and then return to the city he started from, making the whole distance covered the shortest.
