An objective reduction method based on advanced clustering for many-objective optimization problems and its human-computer interaction visualization of pareto front. (July 2021)

• Record Type: Journal Article
• Authors: Ding, Rui; Dong, Hong-bin; Yin, Gui-sheng; Sun, Jing; Yu, Xiao-dong; Feng, Xian-bin
• Is Part Of: Computers & electrical engineering. Volume 93(2021)
• Publication Date: 2021-07
• Subjects: Many-objective optimization -- Human-computer interaction friendliness -- Clustering -- Objective reduction -- Objective aggregation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107266
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: An adaptive clustering method based on propagating tree is proposed for solving MaOP with complex PF shape. The number of clusters is determined adaptively according to the relationships among objectives. The degree of objective reduction is also determined adaptively. The aggregation objectives are used to retain the feature structure of the original problem. An objective reduction method based on adaptive propagating tree clustering for MaOPs is proposed. Abstract: Many-objective optimization problems(MaOPs) are the most challenging problems among multi-objective optimization problems (MOPs). Objective reduction method has become one of the most important technique for MaOPs which can alleviate the difficulties of selection pressure, computational cost and the human-computer interaction visualization. In this paper, we propose an objective reduction algorithm based on adaptive propagating tree clustering for MaOPs. The advanced adaptive clustering method makes the number of clusters determined adaptively, and outliers can be clustered correctly. According to the clustering result, the algorithm uses an adaptive objective aggrega tion method which can preserve the structure of the original problem as much as possible. The algorithm is suitable for dealing with MaOPs with irregular shape of sample sets, and can improve the friendliness of human-computer interaction visualization of Pareto Front. Compared with different types of classical many-objective optimization algorithms, the simulation results of our algorithm has considerable advantages. Graphical abstract: Image, graphical abstract