A Surrogate-Assisted Many-Objective Evolutionary Algorithm Using Multi- Classification and Coevolution for Expensive Optimization Problems
文献类型:期刊论文
作者 | Wang, Ruoyu2; Zhou, Yuee2; Chen HN(陈瀚宁)3; Ma LB(马连博)2; Zheng M(郑萌)1,4 |
刊名 | IEEE ACCESS |
出版日期 | 2021 |
卷号 | 9页码:159160-159174 |
ISSN号 | 2169-3536 |
关键词 | Statistics Sociology Optimization Computational modeling Evolutionary computation Training Linear programming Coevolution expensive optimization multi-classification surrogate-assisted evolutionary algorithm |
产权排序 | 3 |
英文摘要 | Surrogate-assisted evolutionary algorithms have received a surge of attentions for their promising ability of solving expensive optimization problems. Existing surrogate-assisted evolutionary algorithms usually adopt the regression models and the binary classification models to guide the evolution of the population for solving the multiobjective optimization problems. However, the regression models will make the algorithm to be increasingly computation-expensive as the number of objectives increases, while the use of the binary classification models might suffer from the poor diversity since the diversified information of solutions cannot be reflected in these classification models. For this issue, this paper proposes a surrogate-assisted many-objective evolutionary algorithm using the cooperation of the multi-classification and regression models to improve the search quality while reducing the computational cost. Our approach includes two parts: At the model training stage, a multi-classification model is constructed to divide the whole population into several classes for ensuring diversity, a distance regression model and an angle regression model are used to select solutions with better convergence and diversity in each class; At the evolution stage, a coevolutionary framework is used to guide the evolution according to a new selection criterion. Experimental results verify the effectiveness of the proposed algorithm on a set of expensive test problems with up to 10 objectives. |
语种 | 英语 |
WOS记录号 | WOS:000728114700001 |
资助机构 | National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41772123, 62022088] ; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [N2117005] ; Natural Science Foundation of Liaoning ProvinceNatural Science Foundation of Liaoning Province [2021-KF-11-01] |
源URL | [http://ir.sia.cn/handle/173321/30092] |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | Chen HN(陈瀚宁); Ma LB(马连博) |
作者单位 | 1.Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China 2.Software College, Northeastern University, Shenyang 110016, China 3.School of Computer Science and Technology, Tiangong University, Tianjin 300387, China 4.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China |
推荐引用方式 GB/T 7714 | Wang, Ruoyu,Zhou, Yuee,Chen HN,et al. A Surrogate-Assisted Many-Objective Evolutionary Algorithm Using Multi- Classification and Coevolution for Expensive Optimization Problems[J]. IEEE ACCESS,2021,9:159160-159174. |
APA | Wang, Ruoyu,Zhou, Yuee,Chen HN,Ma LB,&Zheng M.(2021).A Surrogate-Assisted Many-Objective Evolutionary Algorithm Using Multi- Classification and Coevolution for Expensive Optimization Problems.IEEE ACCESS,9,159160-159174. |
MLA | Wang, Ruoyu,et al."A Surrogate-Assisted Many-Objective Evolutionary Algorithm Using Multi- Classification and Coevolution for Expensive Optimization Problems".IEEE ACCESS 9(2021):159160-159174. |
入库方式: OAI收割
来源:沈阳自动化研究所
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