Abstract
The Traveling Salesman Problem (TSP), a classic problem in combinatorial optimization, is a well-known NP-hard problem with a wide range of real-world applications. Dynamic TSP is a further upgrade of TSP. Its dynamic change information leads to the greater complexity of the problem. Over the years, numerous excellent algorithms have been proposed by researchers to solve this problem, from the early exact algorithms to approximate algorithms, heuristics, and more recently, machine learning algorithms. However, these algorithms either only work with static TSP or have an unacceptable time consumption. To this end, we propose an optimized pointer network for approximate solution of dynamic TSP, which guarantees a high-quality approximate solution with very low time consumption. We introduce an attention mechanism in our model to fuse the dynamically changing edge information and the statically invariant node coordinate information and use reinforcement learning to enhance the decision-making of the model. Finally, the superior performance for dynamic TSP with low time-cost is verified on comparison experiments.
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Xiao, Z., Lu, M., He, W., Cai, J., Xiong, N.N. (2022). OPN-DTSP: Optimized Pointer Networks for Approximate Solution of Dynamic Traveling Salesman Problem. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_39
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