多目标尺寸与形状优化的伴随变量法

Chen Jian Ken Lee, Wataru Furuya, Masato Tanaka, N. Takano
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引用次数: 4

摘要

基于梯度的优化方法具有光滑的目标函数和约束条件,可以有效地求解多目标优化问题。然而,当应用于结构尺寸优化问题时,使用有限元法(FEM)和有限差分格式来计算灵敏度可能会导致计算成本的增加。采用伴随变量法可以减少计算量。为了有效地解决多目标结构尺寸和形状优化问题,本文提出了伴随变量法。伴随变量法可以有效地计算涉及结构响应的目标的多个灵敏度,并通过减少每个设计变量所需的灵敏度计算次数来降低计算成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adjoint Variable Method for Multi-Objective Sizing and Shape Optimization
With smooth objective functions and constraint conditions, gradient-based methods can be used to solve multi-objective optimization problems efficiently. However, when applied to structural sizing optimization problems, using the Finite Element Method (FEM) and a finite difference scheme to calculate sensitivities can be computationally expensive. The adjoint variable method can be used to reduce computational cost. In order to solve multi-objective structural sizing and shape optimization problems efficiently, this paper proposes using the adjoint variable method. The adjoint variable method efficiently calculates multiple sensitivities for objectives that involve structural responses and cuts down computational cost by reducing the number of sensitivity calculations required per design variable.
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