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题名Objective Extraction for Many-Objective Optimization Problems: Algorithm and Test Problems
作者
发表日期2016-10-01
发表期刊IEEE Transactions on Evolutionary Computation
ISSN/eISSN1089-778X
卷号20期号:5页码:755-772
摘要For many-objective optimization problems (MaOPs), in which the number of objectives is greater than three, the performance of most existing evolutionary multi-objective optimization algorithms generally deteriorates over the number of objectives. As some MaOPs may have redundant or correlated objectives, it is desirable to reduce the number of the objectives in such circumstances. However, the Pareto solution of the reduced MaOP obtained by most of the existing objective reduction methods, based on objective selection, may not be the Pareto solution of the original MaOP. In this paper, we propose an objective extraction method (OEM) for MaOPs. It formulates the reduced objective as a linear combination of the original objectives to maximize the conflict between the reduced objectives. Subsequently, the Pareto solution of the reduced MaOP obtained by the proposed algorithm is that of the original MaOP, and the proposed algorithm can thus preserve the dominance structure as much as possible. Moreover, we propose a novel framework that features both simple and complicated Pareto set shapes for many-objective test problems with an arbitrary number of essential objectives. Within this framework, we can control the importance of essential objectives. As there is no direct performance metric for the objective reduction algorithms on the benchmarks, we present a new metric that features simplicity and usability for the objective reduction algorithms. We compare the proposed OEM with three objective reduction methods, i.e., REDGA, L-PCA, and NL-MVU-PCA, on the proposed test problems and benchmark DTLZ5 with different numbers of objectives and essential objectives. Our numerical studies show the effectiveness and robustness of the proposed approach.
关键词Evolutionary algorithm many-objective optimization objective reduction test problem
DOI10.1109/TEVC.2016.2519758
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语种英语English
Scopus入藏号2-s2.0-84995469154
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被引频次:54[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/6394
专题北师香港浸会大学
作者单位
1.Department of Computer Science,Hong Kong Baptist University (HKBU),999077,Hong Kong
2.Institute of Research and Continuing Education,HKBU,Hong Kong
3.United International College,Beijing Normal University-HKBU,Zhuhai,519000,China
4.Guangdong University of Technology,Guangzhou,510520,China
第一作者单位北师香港浸会大学
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GB/T 7714
Cheung,Yiu Ming,Gu,Fangqing,Liu,Hai Lin. Objective Extraction for Many-Objective Optimization Problems: Algorithm and Test Problems[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(5): 755-772.
APA Cheung,Yiu Ming, Gu,Fangqing, & Liu,Hai Lin. (2016). Objective Extraction for Many-Objective Optimization Problems: Algorithm and Test Problems. IEEE Transactions on Evolutionary Computation, 20(5), 755-772.
MLA Cheung,Yiu Ming,et al."Objective Extraction for Many-Objective Optimization Problems: Algorithm and Test Problems". IEEE Transactions on Evolutionary Computation 20.5(2016): 755-772.
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