Multi-objective design optimization using hybrid search algorithms with interval uncertainty for thin-walled structures

Xu, X; Xu, G; Chen, J; Liu, Z; Chen, X; Zhang, Y; Fang, J; Gao, Y

Multi-objective design optimization using hybrid search algorithms with interval uncertainty for thin-walled structures

Xu, X Xu, G Chen, J Liu, Z Chen, X Zhang, Y Fang, J

Gao, Y

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Thin-Walled Structures, 2022, 175
Issue Date:: 2022-06-01

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Field	Value	Language
dc.contributor.author	Xu, X
dc.contributor.author	Xu, G
dc.contributor.author	Chen, J
dc.contributor.author	Liu, Z
dc.contributor.author	Chen, X
dc.contributor.author	Zhang, Y
dc.contributor.author	Fang, J https://orcid.org/0000-0003-0119-6108
dc.contributor.author	Gao, Y
dc.date.accessioned	2023-04-10T23:12:21Z
dc.date.available	2023-04-10T23:12:21Z
dc.date.issued	2022-06-01
dc.identifier.citation	Thin-Walled Structures, 2022, 175
dc.identifier.issn	0263-8231
dc.identifier.issn	1879-3223
dc.identifier.uri	http://hdl.handle.net/10453/169414
dc.description.abstract	Thin-walled structures have been widely used in vehicle components due to their lightweight characteristics. To design structural parameters that are more in line with actual requirements, it is often necessary to consider the influence of uncertainty in design parameters. An effective multi-objective design optimization method using hybrid search algorithms under interval uncertainty (MDOHSA) is proposed in this study. The MDOHSA mainly consists of the modified gray wolf optimization (mGWO) and the pattern search (PS) algorithms. The mGWO algorithm is applied to derive a new solution set and the PS algorithm is used to quickly search the lower and upper bounds of interval responses in MDOHSA. The non-dominated strategy considering uncertainty is designed by the possibility degree of interval value. The mGWO is designed by improving the initialization population and the updating mechanism. Some standard test functions are implemented to analyze the proposed method, and the result shows that the mGWO algorithm is superior to the other analogous algorithms. In this study, the car subframe, the hexagonal fractal structure, and the bracket of an intelligent sanitation vehicle are optimized by MDOHSA. The above results show that the optimization method proposed in this study is effective. The feasible solution of the multi-objective design optimization method can be selected according to the interval deviation degree of design variables and performance requirements. This study develops an effective multi-objective design optimization method for thin-walled structures under interval uncertainty and can continue to be used for the uncertain optimization of other engineering problems.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Thin-Walled Structures
dc.relation.isbasedon	10.1016/j.tws.2022.109218
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0901 Aerospace Engineering, 0905 Civil Engineering, 0913 Mechanical Engineering
dc.subject.classification	Civil Engineering
dc.title	Multi-objective design optimization using hybrid search algorithms with interval uncertainty for thin-walled structures
dc.type	Journal Article
utslib.citation.volume	175
utslib.for	0901 Aerospace Engineering
utslib.for	0905 Civil Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-10T23:12:19Z
pubs.publication-status	Published
pubs.volume	175

Abstract:

Thin-walled structures have been widely used in vehicle components due to their lightweight characteristics. To design structural parameters that are more in line with actual requirements, it is often necessary to consider the influence of uncertainty in design parameters. An effective multi-objective design optimization method using hybrid search algorithms under interval uncertainty (MDOHSA) is proposed in this study. The MDOHSA mainly consists of the modified gray wolf optimization (mGWO) and the pattern search (PS) algorithms. The mGWO algorithm is applied to derive a new solution set and the PS algorithm is used to quickly search the lower and upper bounds of interval responses in MDOHSA. The non-dominated strategy considering uncertainty is designed by the possibility degree of interval value. The mGWO is designed by improving the initialization population and the updating mechanism. Some standard test functions are implemented to analyze the proposed method, and the result shows that the mGWO algorithm is superior to the other analogous algorithms. In this study, the car subframe, the hexagonal fractal structure, and the bracket of an intelligent sanitation vehicle are optimized by MDOHSA. The above results show that the optimization method proposed in this study is effective. The feasible solution of the multi-objective design optimization method can be selected according to the interval deviation degree of design variables and performance requirements. This study develops an effective multi-objective design optimization method for thin-walled structures under interval uncertainty and can continue to be used for the uncertain optimization of other engineering problems.

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http://hdl.handle.net/10453/169414