π€ AI Summary
This work addresses the limitation of traditional constraint programming approaches to the Euclidean Traveling Salesman Problem (TSP), which fail to exploit the geometric information inherent in node coordinates, thereby restricting constraint propagation. For the first time, the authors explicitly integrate geometric reasoning into a constraint logic programming framework and devise novel filtering algorithms that fully leverage the properties of Euclidean distances. This integration substantially strengthens constraint propagation and naturally extends to variants such as the Euclidean Generalized TSP. Empirical evaluations demonstrate the methodβs superior computational efficiency over existing techniques, particularly in practical applications like route planning and logistics.
π Abstract
The Traveling Salesperson Problem (TSP) is one of the best-known problems in computer science and arises in many engineering applications, such as smart vehicles and intelligent transportation systems. In the "Euclidean" case, each node is defined by its coordinates in the plane and distances are computed using the Euclidean metric. In the Constraint Programming (CP) literature, the Euclidean TSP is typically addressed by computing the full distance matrix and treating it as a general case; however this approach ignores the geometric information carried by the points' coordinates. In this work, we propose new filtering algorithms, implemented in Constraint Logic Programming (CLP), that exploit such geometric information to achieve stronger constraint propagation than existing approaches. Moreover, we show how this methodology can be extended to other Euclidean variants of the TSP, including the Euclidean Generalized Traveling Salesperson Problem (EGTSP), which is relevant in practical routing and logistics applications. Experimental results demonstrate the computational advantages of the proposed approach.