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Learning to Optimize: Reference Vector Reinforcement Learning Adaption to Constrained Many-Objective Optimization of Industrial Copper Burdening System
Ma LB(马连博)1; Li, Nan1; Guo, Yinan2; Wang XW(王兴伟)3; Yang, Shengxiang4; Huang M(黄敏)5; Zhang H(张浩)6
Department数字工厂研究室
Source PublicationIEEE Transactions on Cybernetics
ISSN2168-2267
2021
Pages1-14
Indexed BySCI ; EI
EI Accession number20213010674972
WOS IDWOS:000732887500001
Contribution Rank6
Funding OrganizationNational Natural Science Foundation of China under Grant 61773103, Grant 61973305, Grant 61872073, Grant 71620107003, Grant 61673331, and Grant 62032013 ; Liaoning Revitalization Talents Program under Grant XLYC1902010 and Grant XLYC1802115
KeywordCopper burdening optimization many-objective optimization reference vector reinforcement learning (RVRL)
Abstract

The performance of decomposition-based algorithms is sensitive to the Pareto front shapes since their reference vectors preset in advance are not always adaptable to various problem characteristics with no a priori knowledge. For this issue, this article proposes an adaptive reference vector reinforcement learning (RVRL) approach to decomposition-based algorithms for industrial copper burdening optimization. The proposed approach involves two main operations, that is: 1) a reinforcement learning (RL) operation and 2) a reference point sampling operation. Given the fact that the states of reference vectors interact with the landscape environment (quite often), the RL operation treats the reference vector adaption process as an RL task, where each reference vector learns from the environmental feedback and selects optimal actions for gradually fitting the problem characteristics. Accordingly, the reference point sampling operation uses estimation-of-distribution learning models to sample new reference points. Finally, the resultant algorithm is applied to handle the proposed industrial copper burdening problem. For this problem, an adaptive penalty function and a soft constraint-based relaxing approach are used to handle complex constraints. Experimental results on both benchmark problems and real-world instances verify the competitiveness and effectiveness of the proposed algorithm.

Language英语
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/29350
Collection数字工厂研究室
Corresponding AuthorWang XW(王兴伟); Huang M(黄敏)
Affiliation1.College of Software, Northeastern University, Shenyang 110819, China
2.School of Information Science and Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
3.College of Computer Science, Northeastern University, Shenyang 110819, China
4.School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, U.K.
5.College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
6.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Recommended Citation
GB/T 7714
Ma LB,Li, Nan,Guo, Yinan,et al. Learning to Optimize: Reference Vector Reinforcement Learning Adaption to Constrained Many-Objective Optimization of Industrial Copper Burdening System[J]. IEEE Transactions on Cybernetics,2021:1-14.
APA Ma LB.,Li, Nan.,Guo, Yinan.,Wang XW.,Yang, Shengxiang.,...&Zhang H.(2021).Learning to Optimize: Reference Vector Reinforcement Learning Adaption to Constrained Many-Objective Optimization of Industrial Copper Burdening System.IEEE Transactions on Cybernetics,1-14.
MLA Ma LB,et al."Learning to Optimize: Reference Vector Reinforcement Learning Adaption to Constrained Many-Objective Optimization of Industrial Copper Burdening System".IEEE Transactions on Cybernetics (2021):1-14.
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