Abstract Most of the existing dynamic multi-objective evolutionary algorithms (DMOEAs) are effective, which focuses on searching for the approximation of Pareto-optimal front (POF) with well-distributed in handling dynamic multi-objective optimization problems (DMOPs). Nevertheless, in real-world scenarios, the decision maker (DM) may be only interested in a portion of the corresponding POF (i.e., the region of interest) for different instances, rather than the whole POF. Consequently, a novel DMOEA based decomposition and preference (DACP) is proposed, which incorporates the preference of DM into the dynamic search process and tracks a subset of Pareto-optimal set (POS) approximation with respect to the region of interest (ROI). Due to the presence of dynamics, the ROI, which is defined in which DM gives both the preference point and the neighborhood size, may be changing with time-varying DMOPs. Consequently, our algorithm moves the well-distributed reference points, which are located in the neighborhood range, to around the preference point to lead the evolution of the whole population. When a change occurs, a novel strategy is performed for responding to the current change. Particularly, the population will be reinitialized according to a promising direction obtained by letting a few solutions evolve independently for a short time. Comprehensive experiments show that this approach is very competitivecompared with state-of-the-art methods.