Estimation-based Local Search for Stochastic Combinatorial Optimization

The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsability for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is not responsible for any use that might be made of data appearing in this publication. In recent years, much attention has been devoted to the development of metaheuristics and local search algorithms for tackling stochastic combinatorial optimization problems. This paper focuses on local search algorithms; their effectiveness is greatly determined by the evaluation procedure that is used to select the best of several solutions in the presence of uncertainty. In this paper, we propose an effective evaluation procedure that makes use of empirical estimation techniques. We illustrate our approach and assess its performance on the probabilistic traveling salesman problem. Experimental results on a large set of instances show that our approach can lead to a very fast and highly effective local search algorithms.