An accelerated end-to-end method for solving routing problems
Tianyu Zhu, Xinli Shi, Xiangping Xu, Jinde Cao
Abstract
The application of neural network models to solve combinatorial optimization has recently drawn much attention and shown promising results in dealing with similar problems, like Travelling Salesman Problem. The neural network allows to learn solutions based on given problem instances, using reinforcement learning or supervised learning. In this paper, we present a novel end-to-end method to solve routing problems. In specific, we propose a gated cosine-based attention model (GCAM) to train policies, which accelerates the training process and the convergence of policy. Extensive experiments on different scale of routing problems show that the proposed method can achieve faster convergence of the training process than the state-of-the-art deep learning models while achieving solutions of the same quality.