A Novel Local Search Algorithm for the Traveling Salesman Problem that Exploits Backbones

We present and investigate a new method for the Traveling Salesman Problem (TSP) that incorporates backbone information into the well known and widely applied Lin-Kernighan (LK) local search family of algorithms for the problem. We consider how heuristic backbone information can be obtained and develop methods to make biased local perturbations in the LK algorithm and its variants by exploiting heuristic backbone information to improve their efficacy. We present extensive experimental results, using large instances from the TSP Challenge suite and real-world instances in TSPLIB, showing the significant improvement that the new method can provide over the original algorithms.