点击次数：

论文类型：期刊论文

发表时间：2019-08-01

发表刊物：IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

收录刊物：SCIE、EI

卷号：23

期号：4

页面范围：572-586

ISSN号：1089-778X

关键字：Clustering; incremental machine learning; interacting processes; many-objective optimization; reference vector

摘要：Researches have shown difficulties in obtaining proximity while maintaining diversity for many-objective optimization problems. Complexities of the true Pareto front pose challenges for the reference vector-based algorithms for their insufficient adaptability to the diverse characteristics with no priori. This paper proposes a many-objective optimization algorithm with two interacting processes: cascade clustering and reference point incremental learning (CLIA). In the population selection process based on cascade clustering (CC), using the reference vectors provided by the process based on incremental learning, the nondominated and the dominated individuals are clustered and sorted with different manners in a cascade style and are selected by round-robin for better proximity and diversity. In the reference vector adaptation process based on reference point incremental learning, using the feedbacks from the process based on CC, proper distribution of reference points is gradually obtained by incremental learning. Experimental studies on several benchmark problems show that CLIA is competitive compared with the state-of-the-art algorithms and has impressive efficiency and versatility using only the interactions between the two processes without incurring extra evaluations.