关键词: 多目标优化, 多样性, 演化算法, 聚类算子

Abstract: In this paper, an improved clustering-ranking method is proposed for many-objective optimization, which aims to enhance the diversity of obtained solutions. The improvement of algorithm is consist of two parts, that is two-layer weight vector system and a new clustering operator. Compared with traditional method, the two-layer system is able to create the intermediate weight vectors in the objective space. The combination of boundary layer and inner layer is able to promote the ability of diversity in the objective space. Besides, a new clustering operator is introduced, avoiding too many solutions associated in the specific weight vector. Experimental results have shown the effectiveness of the proposed algorithm. Compared to original algorithm and two other algorithms, the proposed method shows highly competitive performance on test problems with various characteristics.

Key words: many-objective optimization, diversity, evolutionary algorithm, clustering operator