旅行商问题
计算机科学
人工神经网络
组合优化
人工智能
2-选项
Lin–Kernighan启发式
随机神经网络
图形
数学优化
时滞神经网络
算法
理论计算机科学
数学
作者
Yong Shi,Yuanying Zhang
标识
DOI:10.1016/j.procs.2022.01.084
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI