Analysis of a High-Performance TSP Solver on the GPU

Graphical Processing Units have been applied to solve NP-hard problems with no known polynomial time solutions. An example of such a problem is the Traveling Salesman Problem (TSP). The TSP is one of the most commonly studied combinatorial optimization problems and has multiple applications in the areas of engineering, transportation, and logistics. This article presents an improved algorithm for approximating the TSP on fully connected, symmetric graphs by utilizing the GPU. Our approach improves an existing 2-opt hill-climbing algorithm with random restarts by considering multiple updates to the current path found in parallel, and it allows k number of updates per iteration, called k-swap . With our k-swap modification, we show a speed-up over the existing algorithm of 4.5× to 22.9× on data sets ranging from 1,400 to 33,810 nodes, respectively.