Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses
文献类型：期刊论文
| 作者 | Wen-Jing Hong1,2,3; Peng Yang3; Ke Tang3 |
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2021 |
| 卷号 | 18期号:2页码:155-169 |
| ISSN号 | 1476-8186 |
| 关键词 | Large-scale multi-objective optimization high-dimensional search space evolutionary computation evolutionary algorithms scalability |
| DOI | 10.1007/s11633-020-1253-0 |
| 英文摘要 | Large-scale multi-objective optimization problems (MOPs) that involve a large number of decision variables, have emerged from many real-world applications. While evolutionary algorithms (EAs) have been widely acknowledged as a mainstream method for MOPs, most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision variables. More recently, it has been reported that traditional multi-objective EAs (MOEAs) suffer severe deterioration with the increase of decision variables. As a result, and motivated by the emergence of real-world large-scale MOPs, investigation of MOEAs in this aspect has attracted much more attention in the past decade. This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles. From the key difficulties of the large-scale MOPs, the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision variables. From the perspective of methodology, the large-scale MOEAs are categorized into three classes and introduced respectively: divide and conquer based, dimensionality reduction based and enhanced search-based approaches. Several future research directions are also discussed. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/44014]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.Guangdong–Hong Kong–Macao Greater Bay Area Center for Brain Science and Brain–inspired Intelligence, Guangzhou 510515, China 2.Department of Management Science, University of Science and Technology of China, Hefei 230027, China 3.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China |
| 推荐引用方式 GB/T 7714 | Wen-Jing Hong,Peng Yang,Ke Tang. Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses[J]. International Journal of Automation and Computing,2021,18(2):155-169. |
| APA | Wen-Jing Hong,Peng Yang,&Ke Tang.(2021).Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses.International Journal of Automation and Computing,18(2),155-169. |
| MLA | Wen-Jing Hong,et al."Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses".International Journal of Automation and Computing 18.2(2021):155-169. |
入库方式： OAI收割
来源：自动化研究所
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