The relevance of tuning the parameters of metaheuristics . A case study : The vehicle routing problem with stochastic demand

Metaheuristics are a class of promising algorithms for combinatorial optimization. A basic implementation of a metaheuristic typically requires rather little development effort. With a significantly larger investment in the design, implementation, and fine-tuning, metaheuristics can often produce state-of-the-art results. We say that, according to the specific context of applications, either a metaheuristic can be used out-of-the-box, or a custom implementation can be developed. This flexibility is one of the major strengths of metaheuristics but it also hides some possible catches. In particular, it should be noticed that results obtained with out-of-the-box implementations cannot be always generalized to custom ones, and vice versa. As a case study, this paper focuses on the vehicle routing problem with stochastic demand and on five among the most successful metaheuristics—namely, tabu search, simulated annealing, genetic algorithm, iterated local search and ant colony optimization. We show that the relative performance of these algorithms strongly varies whether one considers out-of-the-box implementations or custom ones, in which the parameters are accurately fine-tuned.