.png){kind=icon}
.png){kind=icon}
Return
Strategies for adaptive optimization with aggregation constraints using interior-point methods
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compstruc.2015.02.024.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Constraint-aggregation methods are used in engineering optimization problems to approximately impose a bound on a quantity of interest in a differentiable manner. In this paper, we present strategies to adaptively solve aggregation-constrained problems. These adaptive techniques achieve a tighter bound approximation while also reducing the computational cost of optimization. We focus on two aggregation techniques: Kreisselmeier-Steinhauser (KS) aggregation, and induced exponential aggregation. We demonstrate that the proposed adaptive technique achieves significant computational savings compared to fixed-aggregation methods for a series of stress-constrained mass-minimization problems. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Design optimization
Aggregation-constraints
Kreisselmeier-Steinhauser function
Adaptive optimization
Stress-constrained mass-minimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.8
Papers:
6.2K
Citations:
1.7W
Organization
No organization information available