The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design, overlapping community detection, power dispatch, and unmanned aerial vehicle formation. To address such issues, current approaches focus mainly on problems with regular Pareto front rather than solving the . Considering this situation, we propose a many-objective evolutionary algorithm based on decomposition with (MaOEA/D-DRA) for irregular optimization. The proposed algorithm can dynamically allocate computing resources to different search areas according to different shapes of the problem’s Pareto front. An evolutionary population and an are used in the search process, and information extracted from the is used to guide the evolutionary population to different search regions. The evolutionary population evolves with the to decompose a problem into several subproblems, and all the subproblems are optimized in a collaborative manner. The is updated with the method of . The proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms using a variety of test problems with . Experimental results show that the proposed algorithm outperforms these five algorithms with respect to convergence speed and diversity of population members. By comparison with the weighted-sum approach and penalty-based boundary intersection approach, there is an improvement in performance after integration of the into the proposed algorithm.