Solving Large Traveling Salesman Problems (TSPs) by a Recursive Clustering Algorithm and a Scalable FPGA-Based Implementation

2026-07-23

AI Summary

This work addresses the scalability challenges of solving large-scale Traveling Salesman Problems (TSP) under computational complexity and hardware resource constraints by proposing a recursive clustering approach. The method partitions cities into sub-clusters of at most five nodes and employs an approximation model based on squared Euclidean distances. Intra-cluster tours are optimized using constrained TSP formulations combined with simulated annealing, with entry and exit points anchored at neighboring cluster centroids; inter-cluster connections are similarly resolved via simulated annealing. A novel FPGA-based hardware architecture is introduced, which relies solely on on-chip memory and decouples memory usage from logic resource consumption, thereby enabling high scalability. Compared to existing approaches, this solution significantly increases the solvable problem size and operating frequency while reducing resource utilization, offering enhanced practicality and scalability.

Abstract

The Traveling Salesman Problem (TSP) continues to attract significant research interest due to its critical role in various applications. This paper introduces a recursive clustering approach that divides cities into a limited number of clusters, each containing up to five cities and its own centroid. Constrained TSP and simulated annealing techniques are employed to route cities within each cluster, using the centroids of neighboring clusters as entry and exit points for the routing process. This method offers the benefit of producing accurate and cost-effective routing solutions, due to the reduced number of cities in each cluster. The connections between cluster centroids are established using simulated annealing. The FPGA-based proposed hardware implementation demonstrates the feasibility of routing a large number of cities, as the approach leverages memory to store cluster information. Consequently, the method is independent of the FPGA's logic hardware, and its scalability depends on the FPGA's memory capacity. Furthermore, distance calculations using approximate methods for the FPGA execution are compared and the squared Euclidean is chosen due to its low resources' utilization. Compared to previous methods, the proposed approach can handle more cities for routing, offering better scalability and a higher operating frequency.

Problem

• Traveling Salesman Problem
• Large-scale TSP
• Scalability
• FPGA implementation
• Clustering

Innovation

• recursive clustering
• FPGA-based implementation
• scalable TSP solver
• simulated annealing
• approximate distance calculation