In Distributed Adaptive Metaheuristics Selection (DAMS) methods, each computation node can select, at run-time during the optimization process, one metaheuristic to be executed from a portfolio of available metaheuristics. Within the DAMS framework, we investigate different metaheuristic selection strategies which enable to choose locally at each time step a metaheuristic to execute. We conduct a throughout experimental analysis in order to better understand the accuracy and the behavior of the proposed strategies, as well as their relative performance. In particular, we analyze the impact of sharing metaheuristic performance information between compute nodes and the relative effect on each of the considered distributed selection strategies depending on communication topology. Our experimental analysis is performed on the simple one Max problem, for which the best metaheuristics that should be executed at run-time are known, as well as on the more sophisticated NK-landscapes for which non-linearity can be tuned.