A Many‐Objective Evolutionary Algorithm with Spatial Division and Angle Culling Strategy

Abstract

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 (MaOEA‐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 MaOEA‐SDAC with five other state‐of‐the‐art algorithms on classical benchmark problems, the effectiveness of MaOEA‐SDAC in solving high‐dimensional optimization problems has been verified.