Multi-objective design optimization using hybrid search algorithms with interval uncertainty for thin-walled structures
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-objecti...
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| Published in: | Thin-walled structures Vol. 175; p. 109218 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.06.2022
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| ISSN: | 0263-8231, 1879-3223 |
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| 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.
•An effective multi-objective optimization method using hybrid search algorithms under interval uncertainty is proposed.•The modified grey wolf optimization is designed by improving the initialization population and the updating mechanism.•Thin-walled structures considering modal, dynamic crushing, and static loading are taken as optimization examples. |
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| AbstractList | 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.
•An effective multi-objective optimization method using hybrid search algorithms under interval uncertainty is proposed.•The modified grey wolf optimization is designed by improving the initialization population and the updating mechanism.•Thin-walled structures considering modal, dynamic crushing, and static loading are taken as optimization examples. |
| ArticleNumber | 109218 |
| Author | Chen, Xinbo Liu, Zhe Chen, Jiawei Gao, Yunkai Fang, Jianguang Xu, Gaoxiang Xu, Xiang Zhang, Yong |
| Author_xml | – sequence: 1 givenname: Xiang surname: Xu fullname: Xu, Xiang email: xiangxu@tongji.edu.cn organization: School of Automotive Studies, Tongji University, Shanghai 201804, China – sequence: 2 givenname: Gaoxiang surname: Xu fullname: Xu, Gaoxiang organization: School of Automotive Studies, Tongji University, Shanghai 201804, China – sequence: 3 givenname: Jiawei surname: Chen fullname: Chen, Jiawei organization: School of Mechanical Engineering, Tongji University, Shanghai 201804, China – sequence: 4 givenname: Zhe surname: Liu fullname: Liu, Zhe organization: School of Automotive Studies, Tongji University, Shanghai 201804, China – sequence: 5 givenname: Xinbo surname: Chen fullname: Chen, Xinbo email: chenxinbo@tongji.edu.cn organization: School of Automotive Studies, Tongji University, Shanghai 201804, China – sequence: 6 givenname: Yong surname: Zhang fullname: Zhang, Yong organization: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China – sequence: 7 givenname: Jianguang surname: Fang fullname: Fang, Jianguang organization: School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia – sequence: 8 givenname: Yunkai surname: Gao fullname: Gao, Yunkai organization: School of Automotive Studies, Tongji University, Shanghai 201804, China |
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| Keywords | Thin-walled structures Uncertainty Multi-objective optimization Vehicle structures Design optimization |
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