Many-Objective Evolutionary Algorithms: A Survey

被引：635
|
作者：
Li, Bingdong [1 ]
Li, Jinlong [1 ]
Tang, Ke [1 ]
Yao, Xin [2 ]
机构：
[1] Univ Sci & Technol China, Sch Comp Sci & Technol, USTC Birmingham Joint Res Inst Intelligent Comput, Hefei 230027, Anhui, Peoples R China
[2] Univ Birmingham, Sch Comp Sci, Ctr Excellence Res Computat Intelligence & Applic, Birmingham B15 2TT, W Midlands, England
来源：
ACM COMPUTING SURVEYS | 2015年 / 48卷 / 01期
基金：
中国国家自然科学基金; 英国工程与自然科学研究理事会;
关键词：
Many-objective optimization; evolutionary algorithm; scalability; NONDOMINATED SORTING APPROACH; MULTIOBJECTIVE OPTIMIZATION; GENETIC ALGORITHM; PART I; PERFORMANCE; REDUCTION; SEARCH; DESIGN; MOEA/D; CONVERGENCE;
D O I：
10.1145/2792984
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.
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页数：35
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