A many-objective evolutionary algorithm based on rotated grid

作者：Zou, Juan; Fu, Liuwei^*; Zheng, Jinhua^*; Yang, Shengxiang; Yu, Guo; Hu, Yaru

来源：Applied Soft Computing, 2018, 67: 596-609.

DOI：10.1016/j.asoc.2018.02.031

• Many-objective optimization
• Evolutionary algorithms
• Clustering
• Genetic algorithms
• Multi-objective optimization

Evolutionary optimization algorithms, a meta-heuristic approach, often encounter considerable challenges in many-objective optimization problems (MaOPs). The Pareto-based dominance loses its effectiveness in MaOPs, which are defined as having more than three objectives. Therefore, a more valid selection method is proposed to balance convergence and distribution. This paper proposes an algorithm using rotary grid technology to solve MaOPs (denoted by RGridEA). The algorithm uses the rotating grid to partition the objective space. Instead of using the Pareto non-dominated sorting strategy to layer the population a novel stratified method is used to enhance convergence effectively and make use of the grid to improve distribution and uniformity. Finally, with the other seven algorithm was tested on the test function DTLZ series analysis, confirming RGridEA is effective in resolving MaOPs.

• 出版日期2018-6
• 单位中国人民解放军信息工程大学; 衡阳师范学院; 湘潭大学