Implementing Tabu Search to Exploit Sparsity in ATSP Instances

Abstract

Real life traveling salesman problem (TSP) instances are often large, sparse, and asymmetric. Conventional tabu search implementations for the TSP that have been reported in the literature, almost always deals with small, dense and symmetric instances. In this paper, we outline data structures and a tabu search implementation that takes advantage of such data structures, which can exploit sparsity of a TSP instances, and hence can solve relatively large TSP instances (with up to 3000 nodes) much faster than conventional implementations. We also provide computational experiences with this implementation.