Multi-objective optimization of semi-submersible platforms based on a support vector machine with grid search optimized mixed kernels surrogate model

doi:10.1016/j.oceaneng.2022.112077

	Multi-objective optimization of semi-submersible platforms based on a support vector machine with grid search optimized mixed kernels surrogate model
	Mao, Yixuan 1; Wang, Tianqi 1; Duan, Menglan1 ; Men HY(门弘远)2
通讯作者	Mao, Yixuan(2021310508@student.cup.edu.cn)
发表期刊	OCEAN ENGINEERING
	2022-09-15
卷号	260 页码:20
ISSN	0029-8018
摘要	Determination of optimal hull configurations in the semi-submersible platform (SEMI) should account for several objectives. These objectives are pertinent to hydrodynamic performances of SEMI under wave action but also total structure cost. They are often contradictory and cannot achieve the minimum simultaneously. Hence, a group of relative optimal and balanced solutions is introduced as optimization results, called Pareto-optimal solutions. This paper presents a surrogate-assisted technique to seek the optimal configuration of SEMI for minimal heave and roll response and the lightest weight. Design variables samples are generated by means of multidimensional Ladin hypercube design, and then these inputs are employed for hydrodynamic simulation to acquire the response data. To determine the relationship between objectives and hull structure size, Support Vector Machine with Grid Search optimized mixed kernels (SVM-GSM) is constructed as a surrogate model, and triple verification in terms of errors and robustness warrants its reliability. Three categories of Pareto optimal solutions are obtained by Non-dominated Sorting Genetic Algorithm II (NSGA-II), which correspond to three optimal goals. For optimization results, a presented comprehensive verification approach integrates frequency -domain analysis (FD), time-domain analysis (TD), convergence analysis, and main factors screening. This combination renders sufficient and reliable validation to optimization results. Results from FD and TD for SEMI indicate that the optimized effect of Pareto solutions is satisfactory. Besides, the main influence factors in design variables for hydrodynamic response are screened and investigated. Finally, the ranking of the influence degree of each variable is obtained and evaluated. The proposed framework in this paper provides a comprehensive validation idea for the construction of the surrogate model and optimization results for SEMI hull structure optimization.
关键词	Multi-objective optimization SEMI Surrogate model SVM Hydrodynamic response
DOI	10.1016/j.oceaneng.2022.112077
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000835482400003
关键词[WOS]	TENSION LEG PLATFORM ; EVOLUTIONARY ALGORITHM ; DESIGN OPTIMIZATION ; LATIN ; ANTENNAS
WOS研究方向	Engineering ; Oceanography
WOS类目	Engineering, Marine ; Engineering, Civil ; Engineering, Ocean ; Oceanography
资助项目	National Key Research and Develop- ment Program of China[2016YFC0303701]
项目资助者	National Key Research and Develop- ment Program of China
论文分区	一类
力学所作者排名	3+
RpAuthor	Mao, Yixuan
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/89835
专题	高温气体动力学国家重点实验室
作者单位	1.China Univ Petr, Coll Safety & Ocean Engn, Beijing, Peoples R China; 2.Chinese Acad Sci, Inst Mech, Key Lab High Temp Gas Dynam, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Mao, Yixuan,Wang, Tianqi,Duan, Menglan,et al. Multi-objective optimization of semi-submersible platforms based on a support vector machine with grid search optimized mixed kernels surrogate model[J]. OCEAN ENGINEERING,2022,260:20.
APA	Mao, Yixuan,Wang, Tianqi,Duan, Menglan,&门弘远.(2022).Multi-objective optimization of semi-submersible platforms based on a support vector machine with grid search optimized mixed kernels surrogate model.OCEAN ENGINEERING,260,20.
MLA	Mao, Yixuan,et al."Multi-objective optimization of semi-submersible platforms based on a support vector machine with grid search optimized mixed kernels surrogate model".OCEAN ENGINEERING 260(2022):20.

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