That there is no best initial parameter setting for a metaheuristic on all optimization problems is a proven fact (no free lunch theorem). This paper studies the applicability of so called robust parameter settings for combinatorial optimization problems. Design of Experiments supported parameter screening had been carried out, analyzing a discrete Particle Swarm Optimization algorithm on three demographically very dissimilar instances of the Traveling Salesmen Problem. First experimental results indicate that parameter settings produce varying performance quality for the three instances. The robust parameter setting is outperformed in two out of three cases. The results are even significantly worse when considering quality/time trade-off. A methodology for problem generalization is referred to as a possible solution.