Estimation‐based metaheuristics for the probabilistic traveling salesman problem

Estimation‐based metaheuristics for the probabilistic traveling salesman problem

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Article ID: iaor20123264
Volume: 37
Issue: 11
Start Page Number: 1939
End Page Number: 1951
Publication Date: Nov 2010
Journal: Computers and Operations Research
Authors: , , ,
Keywords: combinatorial optimization, stochastic processes, heuristics: local search, programming: probabilistic
Abstract:

The probabilistic traveling salesman problem (PTSP) is a central problem in stochastic routing. Recently, we have shown that empirical estimation is a promising approach to devise highly effective local search algorithms for the PTSP. In this paper, we customize two metaheuristics, an iterated local search algorithm and a memetic algorithm, to solve the PTSP. This customization consists in adopting the estimation approach to evaluate the solution cost, exploiting a recently developed estimation‐based local search algorithm, and tuning the metaheuristics parameters. We present an experimental study of the estimation‐based metaheuristic algorithms on a number of instance classes. The results show that the proposed algorithms are highly effective and that they define a new state‐of‐the‐art for the PTSP.

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