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In this paper, a dynamic multiobjective evolutionary algorithm (DMOEA) with an adaptive response mechanism selection strategy is proposed to address the shortcoming that a single response mechanism is suitable only for solving a certain type of dynamic multiobjective optimization problem. The proposed algorithm combines an adaptive response mechanism selection (ARMS) strategy and a multiobjective evolutionary algorithm based on decomposition (MOEA/D), and it is denoted as the MOEA/D-ARMS. Unlike the existing approaches, the ARMS strategy can adaptively select effective response mechanisms from the response mechanism pool based on the recent performance of each response mechanism. Four representative response mechanisms are selected to form the response mechanism pool. An overall evaluation strategy that assigns rewards to the response mechanism is adopted, and a probability-based method that is used to decide which response mechanism can be used to generate a new solution is employed. The proposed MOEA/D-ARMS algorithm is tested on two groups of test instances and compared with the decomposition-based and dominance-based DMOEAs. The results of the proposed MOEA/D-ARMS algorithm are superior to the compared algorithms, demonstrating its effectiveness. • This paper proposed an adaptive response mechanism selection (ARMS) framework and a dynamic MOEA. • The ARMS framework can adaptively select the most effective response mechanisms based on their recent performance. • An overall evaluation strategy that assigned rewards to the response mechanism was adopted. • The simple ARMS framework can easily incorporate more response mechanisms. • The statistical results clearly demonstrated that MOEA/D-ARMS were superior to the compared algorithms. [ABSTRACT FROM AUTHOR]