  Meta-heuristics are frequently used to tackle NP-hard combinatorial
optimization problems. With this paper we contribute to the understanding of
the success of 2-opt based local search algorithms for solving the traveling
salesman problem (TSP). Although 2-opt is widely used in practice, it is hard
to understand its success from a theoretical perspective. We take a statistical
approach and examine the features of TSP instances that make the problem either
hard or easy to solve. As a measure of problem difficulty for 2-opt we use the
approximation ratio that it achieves on a given instance. Our investigations
point out important features that make TSP instances hard or easy to be
approximated by 2-opt.
