Many-objective optimization problems (MaOPs) are a great challenge for multi-objective evolutionary algorithms (MOEAs) and lately, several MOEAs have been proposed. Each MOEA uses different algorithmic components during the search process and performs differently. Therefore, there is no single algorithm able to achieve the best results in all problems. The collaboration of multiple MOEAs and the use of hyperheuristics can help to create a searchability able to achieve good results in a wide range of problem instances. In this context, this research proposes a model for collaboration of MOEAs guided by hyper-heuristic, called HHcMOEA. In HHcMOEA, the hyper-heuristic controls and mix MOEAs, automatically deciding which one to apply during the search process. On the other hand, HHcMOEA also incorporates exchange of information between the MOEAs. And, a fitness improvement rate metric, based on the R2 indicator to decide about the quality of the application of an MOEA. HHcMOEA is implemented using a set of MOEAs with diverse characteristics. An experiment is used to evaluate HHcMOEA in two versions: with and without information exchange. Although, the two versions of HHcMOEA are compared to the MOEAs applied alone. The empirical evaluation used a set of benchmark problems with different properties. The proposed model achieved the best result or equivalent to the best in almost all problems. Still, the results were deteriorated when the information exchange strategy was not used.