Optimization of the nested Monte-Carlo algorithm on the traveling salesman problem with time windows

Abstract

The traveling salesman problem with time windows is known to be a really difficult benchmark for optimization algorithms. In this paper, we are interested in the minimization of the travel cost. To solve this problem, we propose to use the nested Monte-Carlo algorithm combined with a Self-Adaptation Evolution Strategy. We compare the efficiency of several fitness functions. We show that with our technique we can reach the state of the art solutions for a lot of problems in a short period of time. © 2011 Springer-Verlag.