Evaluation of parallel metaheuristics

When evaluating algorithms a very important goal is to perform better than the state-of-the-art techniques.. This requires experimental tests to compare the new algorithm with respect to the rest. It is, in general, hard to make fair comparisons between algorithms such as metaheuristics. The reason is that we can infer di erent conclusions from the same results depending on the metrics we use and how they are applied. This is specially important for non-deterministic methods. This analysis becomes more complex if the study includes parallel metaheuristics, since many researchers are not aware of existing parallel metrics and their meanings, especially concerning the vast literature on parallel programming used well before metaheuristics were rst introduced. In this paper, we focus on the evaluation of parallel algorithms. We give a clear de nition of the main parallel performance metrics and we illustrate how they can be used.