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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Optimization of the Nested Monte-Carlo Algorithm on the Traveling Salesman Problem with Time Windows
verfasst von : Arpad Rimmel, Fabien Teytaud, Tristan Cazenave
Erschienen in: Applications of Evolutionary Computation
Verlag: Springer Berlin Heidelberg
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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.