Dynamic multi-objective problems represent a challenge in fields such as labor, economics, and industry, due to the changing nature of the objectives or constraints over time. To address these challenges, various strategies have been proposed to improve the performance of algorithms under dynamic conditions. However, to our knowledge, no strategy has been proposed combining two specific approaches to adapting to change: one based on memory, and another based on introducing diversity. This paper presents the hybridization of these memory and diversity introduction strategies within a dynamic cooperative coevolutionary algorithm based on MOEA/D (D-CC-MOEA/D). The memory-based strategy preserves populations from previous generations to function as an external population (EP) during environmental changes, while the diversity-based strategy introduces variability through selective mutations. D-CC-MOEA/D solves FDA dynamic problems, where FDA may refer to Farina, Deb & Amato. Currently this algorithm version of algorithm solves FDA1 and FDA3 problems, using MOEA-D as the solver, random selection operators, SBX crossover, and polynomial mutation in each species. The results so far show that the hybrid strategy allows the algorithm to adapt to changes in the problem effectively, maintaining adequate diversity among them. To identify effective adaptation, we used the hypervolume of each of the five fronts for FDA1 and FDA3, finding that, on average, diversity is maintained in both problems between 74 and 95%.