A Generative Graph Method to Solve the Travelling Salesman Problem
This addresses a challenging combinatorial optimization problem for researchers and practitioners in AI and operations research, but appears incremental as it builds on existing graph learning techniques.
The paper tackled the Travelling Salesman Problem (TSP) by proposing a generative Graph Learning Network (GLN) to approximate solutions, achieving a low optimality gap with significant computation savings compared to optimal methods.
The Travelling Salesman Problem (TSP) is a challenging graph task in combinatorial optimization that requires reasoning about both local node neighborhoods and global graph structure. In this paper, we propose to use the novel Graph Learning Network (GLN), a generative approach, to approximately solve the TSP. GLN model learns directly the pattern of TSP instances as training dataset, encodes the graph properties, and merge the different node embeddings to output node-to-node an optimal tour directly or via graph search technique that validates the final tour. The preliminary results of the proposed novel approach proves its applicability to this challenging problem providing a low optimally gap with significant computation saving compared to the optimal solution.