In a specific project, how to find a reasonable balance between a plurality of objectives and their optimal solutions has always been an important aspect for researchers. As a trade off between fast convergence and a rich diversity, a Many-objective evolutionary algorithm based on a spatial division and angle-culling strategy（Ma OEA-SDAC） is proposed. In the reorganization stage, a restricted matching selection can enhance the reproductivity. In the environment selection stage, a space division and angle-based elimination strategy can effectively improve the convergence and diversity imbalance of its solution set. Through detailed experiments and a comparative analysis of the proposed Ma OEA-SDAC with five other state-of-the-art algorithms on classical benchmark problems, the effectiveness of Ma OEA-SDAC in solving high-dimensional optimization problems has been verified.
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