Correcting misinterpretations on the distribution of feasible solution lengths in the traveling salesman problem

Abstract

The traveling salesman problem (TSP) is the canonical combinatorial optimization problem famous throughout literature.  There exists an objective function associated with every feasible solution.  However, the increase in the number of possible solutions makes this an NP-Hard problem.  We show that the central limit theorem (CLT) applies to the problem.  We then conduct extensive computational testing to show that the cycle lengths tend to a normal distribution as the problem grows large.  When the size of the TSP problem exceeds computational power, better understanding solution distributions allows us to save resources.  This is a non-trivial result as understanding solution distributions in huge TSP problems helps us to minimize computational effort that may not lead to significantly better results.