Indexed by:Journal Papers
Date of Publication:2019-08-01
Journal:IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Included Journals:SCIE、EI
Volume:23
Issue:4
Page Number:572-586
ISSN No.:1089-778X
Key Words:Clustering; incremental machine learning; interacting processes; many-objective optimization; reference vector
Abstract: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.
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Main positions:计算机科学与技术学院党委书记
Gender:Male
Alma Mater:吉林大学
Degree:Doctoral Degree
School/Department:计算机科学与技术学院
Discipline:Computer Applied Technology
Business Address:创新园大厦A832
Contact Information:hwge@dlut.edu.cn
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