In recent years, metaheuristic algorithms have been extensively used to solve global optimization problems. In general, they are used in complex problems where a good solution is needed, especially in cases with incomplete or imperfect information. Recently, the concept of efficiently combine meta-heuristics has emerged, in a field called hybridization of meta-heuristics. These hybrid systems have been successfully applied in traditional optimization problems. In this paper, a hybrid system, called MAMH (Multi-agent Metaheuristic Hybridization) is adapted to combine trajectory-based metaheuristics and to be applied in the design of ensemble systems. The main goal of this paper is to evaluate the use of hybrid trajectory-based meta-heuristics applied to the design of ensembles of classifiers. In order to validate the feasibility of using MAMH as ensemble generator, an empirical analysis will be conducted, in which a comparative analysis between MAMH and traditional trajectory-based metaheuristics will be performed. Our findings indicated a competitive performance of MAMH, with the best performance for the most important objective function.