A Gpu Implementation of Local Search Operators for Symmetric Travelling Salesman Problem

The Travelling Salesman Problem (TSP) is one of the most studied combinatorial optimization problem which is significant in many practical applications in transportation field. The TSP is an NP-hard problem and requires large computational power to be optimally solved by exact algorithms. In the past few years, fast development of generalpurpose Graphics Processing Units (GPUs) has brought huge improvement in decreasing the algorithms execution time. In this paper, we implement 2-opt and 3-opt local search operators for solving the TSP on the GPU using its respective application programming interface. The novelty presented in this paper is a new parallel iterated local search implementation with 2-opt and 3-opt operators for symmetric TSP, optimized for the execution on GPUs. With our implementation large TSP problems (up to 85,900 cities) can be solved using the GPU. We show that our GPU implementation can be up to 27 times faster than central processing unit (CPU) implementation without losing solution quality for TSPlib problems as well as for our CRO TSP problem.