当前位置: X-MOL 学术Struct. Multidisc. Optim. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Constrained space-filling and non-collapsing sequential design of experiments and its application for the lightweight design of cylindrical stiffened shells
Structural and Multidisciplinary Optimization ( IF 3.6 ) Pub Date : 2021-09-18 , DOI: 10.1007/s00158-021-02948-6
Zhixiang Wang 1 , Dapeng Zhang 1 , Yongjun Lei 1 , Zeping Wu 1 , Jie Wang 2 , Xing OuYang 2 , Jun Wang 2
Affiliation  

Constraints widely exist in the structural optimization problems, including not only expensive constraints but also inexpensive constraints with explicit or implicit analytical expressions. Simply omitting the infeasible sampling points generated by conventional design of experiments (DoE) techniques leads to fewer feasible points than desired and to the remaining points distributed sub-optimally. This paper presents a novel constrained space-filling and non-collapsing sequential sampling (CSFSS) method for the arbitrarily constrained design space. To this end, an improved local density is firstly proposed to measure the spatial distribution of the sampling points located in the feasible region. Then, a novel formulation based on the improved local density is proposed for the criterion of sequential sampling strategies, which can guarantee the space-filling and non-collapsing properties both preferably optimal. Afterwards, several methods are proposed to tackle the challenging multimodal optimization problem posed by sequential sampling strategies. Finally, a replacement-based strategy is proposed to further elevate the quality of the design. Extensive numerical results, including an application for the lightweight design of cylindrical stiffened shells in aerospace engineering, highlight the satisfying performance of the proposed method not only in obtaining a high quality of constrained experimental designs but also in competitive contributions to constrained optimization gains.



中文翻译:

约束空间填充非塌陷实验序列设计及其在圆柱加筋壳轻量化设计中的应用

约束广泛存在于结构优化问题中,不仅包括昂贵的约束,还包括具有显式或隐式解析表达式的廉价约束。简单地省略由传统实验设计 (DoE) 技术生成的不可行采样点会导致可行点少于预期,并且剩余点分布不理想。本文提出了一种用于任意约束设计空间的新型约束空间填充和非折叠顺序采样 (CSFSS) 方法。为此,首先提出了一种改进的局部密度来衡量位于可行区域内的采样点的空间分布。然后,提出了一种基于改进局部密度的新公式作为顺序采样策略的标准,可以保证空间填充性和非塌陷性均是最佳的。之后,提出了几种方法来解决由顺序采样策略带来的具有挑战性的多模态优化问题。最后,提出了一种基于替换的策略,以进一步提高设计的质量。广泛的数值结果,包括在航空航天工程中圆柱加筋壳的轻量化设计的应用,突出了所提出的方法的令人满意的性能,不仅在获得高质量的约束实验设计方面,而且在对约束优化收益的竞争性贡献方面。提出了几种方法来解决由顺序采样策略带来的具有挑战性的多模态优化问题。最后,提出了一种基于替换的策略,以进一步提高设计的质量。广泛的数值结果,包括在航空航天工程中圆柱加筋壳的轻量化设计的应用,突出了所提出的方法的令人满意的性能,不仅在获得高质量的约束实验设计方面,而且在对约束优化收益的竞争性贡献方面。提出了几种方法来解决由顺序采样策略带来的具有挑战性的多模态优化问题。最后,提出了一种基于替换的策略,以进一步提高设计的质量。广泛的数值结果,包括在航空航天工程中圆柱加筋壳的轻量化设计的应用,突出了所提出的方法的令人满意的性能,不仅在获得高质量的约束实验设计方面,而且在对约束优化收益的竞争性贡献方面。

更新日期:2021-09-19
down
wechat
bug