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Novel Interactive Preference-Based Multiobjective Evolutionary Optimization for Bolt Supporting Networks
Guo YN(郭一楠)1,2; Zhang, Xu3; Gong DW(巩敦卫)1,4; Zhang, Zhen1; Yang JJ(杨建建)5
Department机器人学研究室
Source PublicationIEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
ISSN1089-778X
2020
Volume24Issue:4Pages:750-764
Indexed BySCI ; EI
EI Accession number20203509110788
WOS IDWOS:000554887000010
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of ChinaNational Natural Science Foundation of China [61973305, 61573361, 61773384] ; Six Talent Peaks Project in Jiangsu Province [2017-DZXX-046] ; State Key Laboratory of Robotics, China [2019-O12] ; National Key Research and Development Program of China [2018YFB1003802-01]
KeywordFasteners Optimization Rocks Stability analysis Tunneling Bolt supporting network interaction multiobjective evolutionary optimization preference surrogate model
Abstract

Previous methods of designing a bolt supporting network, which depend on engineering experiences, seek optimal bolt supporting schemes in terms of supporting quality. The supporting cost and time, however, have not been considered, which restricts their applications in real-world situations. We formulate the problem of designing a bolt supporting network as a three-objective optimization model by simultaneously considering such indicators as quality, economy, and efficiency. Especially, two surrogate models are constructed by support vector regression for roof-to-floor convergence and the two-sided displacement, respectively, so as to rapidly evaluate supporting quality during optimization. To solve the formulated model, a novel interactive preference-based multiobjective evolutionary algorithm is proposed. The highlight of generic methods which interactively articulate preferences is to systematically manage the regions of interest by three steps, that is, "partitioning-updating-tracking" in accordance with the cognition process of human. The preference regions of a decision-maker (DM) are first articulated and employed to narrow down the feasible objective space before the evolution in terms of nadir point, not the commonly used ideal point. Then, the DM's preferences are tracked by dynamically updating these preference regions based on satisfactory candidates during the evolution. Finally, individuals in the population are evaluated based on the preference regions. We apply the proposed model and algorithm to design the bolt supporting network of a practical roadway. The experimental results show that the proposed method can generate an optimal bolt supporting scheme with a good balance between supporting quality and the other demands, besides speeding up its convergence.

Language英语
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS KeywordGENETIC ALGORITHMS
WOS Research AreaComputer Science
Funding ProjectNational Natural Science Foundation of China[61973305] ; National Natural Science Foundation of China[61573361] ; National Natural Science Foundation of China[61773384] ; Six Talent Peaks Project in Jiangsu Province[2017-DZXX-046] ; State Key Laboratory of Robotics, China[2019-O12] ; National Key Research and Development Program of China[2018YFB1003802-01]
Citation statistics
Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27479
Collection机器人学研究室
Corresponding AuthorGong DW(巩敦卫)
Affiliation1.School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.State key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.Dongfang Electronics Company, Ltd., Yantai 264000, China
4.School of Information Engineering, Xiangtan University, Xiangtan 411105, China
5.School of Mechanical Electronic and Information Engineering, China University of Mining and Technology at Beijing, Beijing 100083, China
Recommended Citation
GB/T 7714
Guo YN,Zhang, Xu,Gong DW,et al. Novel Interactive Preference-Based Multiobjective Evolutionary Optimization for Bolt Supporting Networks[J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,2020,24(4):750-764.
APA Guo YN,Zhang, Xu,Gong DW,Zhang, Zhen,&Yang JJ.(2020).Novel Interactive Preference-Based Multiobjective Evolutionary Optimization for Bolt Supporting Networks.IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,24(4),750-764.
MLA Guo YN,et al."Novel Interactive Preference-Based Multiobjective Evolutionary Optimization for Bolt Supporting Networks".IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 24.4(2020):750-764.
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