Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

被引：96

作者：

Tian, Ye ^{[1
]}

Wang, Handing ^{[2
]}

Zhang, Xingyi ^{[1
]}

Jin, Yaochu ^{[2
]}

机构：

[1] Anhui Univ, Sch Comp Sci & Technol, Inst Bioinspired Intelligence & Min Knowledge, Hefei 230601, Anhui, Peoples R China

[2] Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, Surrey, England

来源：

COMPLEX & INTELLIGENT SYSTEMS | 2017年 / 3卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Non-dominated sorting; Evolutionary algorithm; Multi-objective optimization; Many-objective optimization; MULTIOBJECTIVE OPTIMIZATION; NSGA-II; ALGORITHM; DECOMPOSITION; CONVERGENCE; HYPERVOLUME; OPTIMALITY;

D O I：

10.1007/s40747-017-0057-5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Since non-dominated sorting was first adopted in NSGA in 1995, most evolutionary algorithms have employed non-dominated sorting as one of the major criteria in their environmental selection for solving multi- and many-objective optimization problems. In this paper, we focus on analyzing the effectiveness and efficiency of non-dominated sorting in multi- and many-objective evolutionary algorithms. The effectiveness of non-dominated sorting is verified by considering two popular evolutionary algorithms, NSGA-II and KnEA, which were designed for solving multi- and many-objective optimization problems, respectively. The efficiency of non-dominated sorting is evaluated by comparing several state-of-the-art non-dominated sorting algorithms for multi- and many-objective optimization problems. These results provide important insights to adopt non-dominated sorting in developing novel multi- and many-objective evolutionary algorithms.

引用

页码：247 / 263

页数：17

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