Automatic hyperparameter tuning of topology optimization algorithms using surrogate optimization
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158_2024_Article_3850.pdf
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2.28 MB
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Adobe PDF
Checksum (MD5)
3971a11008402508f3b98c94b300b691
Author(s)
Ha, Dat
Carstensen, Josephine
Date Issued
September 4, 2024
Journal
Structural and Multidisciplinary Optimization
Publisher
Springer Berlin Heidelberg
Citation
Ha, D., Carstensen, J. Automatic hyperparameter tuning of topology optimization algorithms using surrogate optimization. Struct Multidisc Optim 67, 157 (2024).
Version
Final published version
Abstract
This paper presents a new approach that automates the tuning process in topology optimization of parameters that are traditionally defined by the user. The new method draws inspiration from hyperparameter optimization in machine learning. A new design problem is formulated where the topology optimization hyperparameters are defined as design variables and the problem is solved by surrogate optimization. The new design problem is nested, such that a topology optimization problem is solved as an inner problem. To encourage the identification of high-performing solutions while limiting the computational resource requirements, the outer objective function is defined as the original objective combined with penalization for intermediate densities and deviations from the prescribed material consumption. The contribution is demonstrated on density-based topology optimization with various hyperparameters and objectives, including compliance minimization, compliant mechanism design, and buckling load factor maximization. Consistent performance is observed across all tested examples. For a simple two hyperparameter case, the new framework is shown to reduce amount of times a topology optimization algorithm is executed by 90% without notably sacrificing the objective compared to a rigorous manual grid search.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s00158-024-03850-7
