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Strategies for adaptive optimization with aggregation constraints using interior-point methods
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DOI：10.1016/j.compstruc.2015.02.024.png)
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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.
Keyword:
Design optimization
Aggregation-constraints
Kreisselmeier-Steinhauser function
Adaptive optimization
Stress-constrained mass-minimization
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