Optimization of the Nested Monte-Carlo Algorithm on the Traveling Salesman Problem with Time Windows

Arpad Rimmel (1) , Fabien Teytaud (2, 3) , Tristan Cazenave (1, 4)

1 Université Paris Dauphine-PSL
2 LRI - Laboratoire de Recherche en Informatique
3 TAO - Machine Learning and Optimisation
4 LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision

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.

Domaines: Apprentissage [cs.LG]; Optimisation et contrôle [math.OC]

Arpad Rimmel, Fabien Teytaud, Tristan Cazenave. Optimization of the Nested Monte-Carlo Algorithm on the Traveling Salesman Problem with Time Windows. Evostar, Apr 2011, Turin, Italy. ⟨inria-00563668⟩