Tuning a parametric Clarke–Wright heuristic via a genetic algorithm

Almost all heuristic optimization procedures require the presence of a well–tuned set of parameters. The tuning of these parameters is usually a critical issue and may entail intensive computational requirements. We propose a fast and effective approach composed of two distinct stages. In the first stage, a genetic algorithm is applied to a small subset of representative problems to determine a few robust parameter sets. In the second stage, these sets of parameters are the starting points for a fast local search procedure, able to more deeply investigate the space of parameter sets for each problem to be solved. This method is tested on a parametric version of the Clarke and Wright algorithm and the results are compared with an enumerative parameter–setting approach previously proposed in the literature. The results of our computational testing show that our new parameter–setting procedure produces results of the same quality as the enumerative approach, but requires much shorter computational time.