Design Improvement for Complex Systems with Uncertainty

被引:0
作者
Chen, Yue [1 ]
Shi, Jian [2 ,3 ]
Yi, Xiao-Jian [4 ]
机构
[1] Capital Univ Econ & Business, Sch Stat, Beijing 100070, Peoples R China
[2] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100864, Peoples R China
[3] Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
[4] Beijing Inst Technol, Sch Mechatron Engn, Beijing 100811, Peoples R China
基金
中国国家自然科学基金;
关键词
robustness; hyper-box; uncertainty; optimization; key parameters; constraints; OPTIMIZATION; EVOLUTIONARY; PARTITIONS; SELECTION; SET;
D O I
10.3390/math9111173
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The uncertainty of the engineering system increases with its complexity, therefore, the tolerance to the uncertainty becomes important. Even under large variations of design parameters, the system performance should achieve the design goal in the design phase. Therefore, engineers are interested in how to turn a bad design into a good one with the least effort in the presence of uncertainty. To improve a bad design, we classify design parameters into key parameters and non-key parameters based on engineering knowledge, and then seek the maximum solution hyper-box which already includes non-key parameters of this bad design. The solution hyper-box on which all design points are good, that is, they achieve the design goal, provides target intervals for each parameter. The bad design can be turned into a good one by only moving its key parameters into their target intervals. In this paper, the PSO-Divide-Best method is proposed to seek the maximum solution hyper-box which is in compliance with the constraints. This proposed approach has a considerably high possibility to find the globally maximum solution hyper-box that satisfies the constraints and can be used in complex systems with black-box performance functions. Finally, case studies show that the proposed approach outperforms the EPCP and IA-CES methods in the literature.
引用
收藏
页数:20
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