The Traveling Salesman Problem (TSP) is a well-known combinatorial
optimization problem with broad real-world applications. Recently, neural
networks have gained popularity in this research area because they provide
strong heuristic solutions to TSPs. Compared to autoregressive neural
approaches, non-autoregressive (NAR) networks exploit the inference parallelism
to elevate inference speed but suffer from comparatively low solution quality.
In this paper, we propose a novel NAR model named NAR4TSP, which incorporates a
specially designed architecture and an enhanced reinforcement learning
strategy. To the best of our knowledge, NAR4TSP is the first TSP solver that
successfully combines RL and NAR networks. The key lies in the incorporation of
NAR network output decoding into the training process. NAR4TSP efficiently
represents TSP encoded information as rewards and seamlessly integrates it into
reinforcement learning strategies, while maintaining consistent TSP sequence
constraints during both training and testing phases. Experimental results on
both synthetic and real-world TSP instances demonstrate that NAR4TSP
outperforms four state-of-the-art models in terms of solution quality,
inference speed, and generalization to unseen scenarios.Comment: 14 pages, 5 figure