Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230700222-8.doi: 10.11896/jsjkx.230700222

• Artificial Intelligenc • Previous Articles     Next Articles

New Solution for Traveling Salesman Problem Based on Graph Convolution and AttentionNeural Network

WEI Niannian, HAN Shuguang   

  1. School of Science,Zhejiang Sci-Tech University,Hangzhou 310018,China
  • Published:2024-06-06
  • About author:WEI Niannan,born in 1998,bachelor,postgraduate.Her main research in-terests include logistics and supply chain management,and algorithm design.
    HAN Shuguang,born in 1977,Ph.D,professor.His main research interests include logistics and supply chain ma-nagement,and algorithm design.
  • Supported by:
    National Natural Science Foundation of China(12071436).

Abstract: Traveling salesman problem is a classic combinatorial optimization problem.To solve the problem quickly,a learning branch rule is designed,which is based on a deep learning model composed of graph embedding network,graph convolutional neural network,attention neural network and multi-layer perceptron,and the traditional branch and bound algorithm is modified to improve the algorithm performance.Traveling salesman problem instances of 15 cities are supervised and trained ,and the traveling salesman problem instances of 10,15,20,25 and 30 cities are tested on the SCIP solver respectively.We find that the solution time of the branch and bound algorithm based on learning branch rule is -0.0022s,0.0178s,1.7643s,2.3074s,and 2.0538s faster than that of the algorithm based on traditional branch rules,respectively.Therefore,the selection of branch variables based on graph neural networks is effective in improving traditional branch rules and can be well normalized to traveling salesman problem instances with larger training scales.

Key words: Traveling salesman problem, Graph convolutional neural network, Attention neural network, Branch and bound algorithm, Supervised learning

CLC Number: 

  • TP183
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