In combinatorial optimization, Traveling Salesman Problem (TSP) is a well-known issue and one of the key problem in the logistics industry in recent years. The speed, accuracy, and generalization ability of the traditional method and the algorithm design of the specific problem are greatly affected. With the wide application of deep reinforcement learning (DRL) in industry, the automatic design of learning algorithms using DRL models has become a recent research hotspot. This paper proposes a hybrid network model with a spatial attention mechanism to resolve large-scale TSP in order to enhance the generalization capability of DRL-based model on large-scale TSP. Spatial attention is beneficial to capture the global connection between nodes and then to calculate and extract key features by weighting all local features, which is beneficial to improving the generalization ability of the model. The experimental results demonstrate that our model can significantly increase the route problem's optimization effectiveness.
Combinatorial optimization has found its way into a variety of domains, including artificial intelligence and cybernetics. Deep Reinforcement Learning (DRL) has recently demonstrated its promise for developing heuristics for NP-hard routing problems. The current generalization performance of models needs to be improved, especially for large-scale routing problems. In this paper, we propose a hybrid approach for the Capacitated Vehicle Routing Problem (CVRP) based on DRL and adaptive large neighborhood search. The information representation of the neural network for CVRP is also improved by the combination of multi-head attention mechanism, pointer network and graph neural networks. The experimental results demonstrate that the optimization of our model on CVRP outperforms existing DRL techniques and some traditional algorithms. In addition, our method improves the training efficiency of the model and the performance of generalization to large-scale CVRP.
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