A comprehensive survey on NSGA-II for multi-objective optimization and applications

被引：66

作者：

Ma, Haiping ^{[1
]}

Zhang, Yajing ^{[1
]}

Sun, Shengyi ^{[1
]}

Liu, Ting ^{[1
]}

Shan, Yu ^{[1
]}

机构：

[1] Shaoxing Univ, Dept Elect Engn, Shaoxing 312000, Zhejiang, Peoples R China

来源：

ARTIFICIAL INTELLIGENCE REVIEW | 2023年 / 56卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Multi-objective optimization; NSGA-II; Evolutionary algorithm; Non-dominated sorting; SORTING GENETIC ALGORITHM; MANY-OBJECTIVE OPTIMIZATION; TUBE HEAT-EXCHANGERS; PROCESS PARAMETERS OPTIMIZATION; ADME PROPERTIES EVALUATION; DRUG DISCOVERY PREDICTION; SUPPLY CHAIN PROBLEM; EVOLUTIONARY ALGORITHM; OPTIMAL-DESIGN; HYBRID METHOD;

D O I：

10.1007/s10462-023-10526-z

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the last two decades, the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) has attracted extensive research interests, and it is still one of the hottest research methods to deal with multi-objective optimization problems. Considering the importance and wide applications of NSGA-II method, we believe it is the right time to provide a comprehensive survey of the research work in this area, and also to discuss the potential in the future research. The purpose of this paper is to summarize and explore the literature on NSGA-II and another version called NSGA-III, a reference-point based many-objective NSGA-II approach. In this paper, we first introduce the concept of multi-objective optimization and the foundation of NSGA-II. Then we review the family of NSGA-II and their modifications, and classify their applications in engineering community. Finally, we present several interesting open research directions of NSGA-II for multi-objective optimization.

引用

页码：15217 / 15270

页数：54

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