The Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) has obtained very good results on various multi-objective optimization problems in the past few years. This paper focuses on an attempt to improve even more its performance by introducing a hyper-heuristic mechanism to select the best set of its operators and parameters. In this paper we use Upper Confidence Bound (UCB) as the basis of the hyper-heuristic, and test three versions of the proposed approach. Four well known benchmarks (CEC 2009, WFG, DTLZ and ZDT) and a quality indicator (hypervolume) are used to analyze the performance of the three variants. The proposed approach is compared with the original MOEA/D-DRA and the results show that tuning the parameters via UCB is an interesting alternative for a hyper-heuristic based version of MOEA/D-DRA on the addressed problems.