An experimental study of estimation-based metaheuristics for the probabilistic traveling salesman problem

The probabilistic traveling salesman problem (PTSP), a paradigmatic example of a stochastic combinatorial optimization problem, is used to study routing problems under uncertainty. Recently, we introduced a new estimation-based iterative improvement algorithm for the PTSP and we showed that it outperforms for a number of instance classes the previous state-of-the-art algorithms. In this paper, we integrate this estimation-based iterative improvement algorithm into some metaheuristics to solve the PTSP and we study their performance.