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

• 作者: WANG Hongbo YANG Fan TIAN Kena TU Xuyan
• 来源期刊: 电子学报（英文版）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: COMPUTERS AND MICROELECTRONICS
• 页码范围: 437-443
• 页数: 7页
• 语种: 英文
• DOI: 10.1049/cje.2021.03.006

摘要

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.