On the scalability of population restart mechanisms on large-scale global optimization

Population restart mechanisms are a popular method to avoid premature convergence in Evolutionary Algorithms. Many different methods have used these mechanisms in the past in different scenarios. However, most of these works tend to design an ad-hoc population restart approach for the problem under consideration. Furthermore, the effects of the alternative restart strategies and the scalability of the method are rarely analyzed in the literature. In this paper, we conduct a comparative study of 36 population restart strategies (37 if we account for the baseline of not restarting the population) on the SOCO 2011 benchmark, a testbed of 19 continuous scalable functions widely accepted in the continuous optimisation community and that allow an analysis at different problem sizes which is not possible with other existing benchmarks. The results obtained clearly show that there is a relationship between the particular strategy considered and the effectiveness of the method. Moreover, this effectiveness tends to decrease as the dimensionality (complexity) of the problem grows.