Surrogate-based optimization based on the probability of feasibility

Sohst, Martin; Afonso, Frederico; Suleman, Afzal

doi:10.1007/s00158-021-03134-4

Cited by 6 publications

(1 citation statement)

References 38 publications

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“…One way is to use a probability of feasibility to account for the mean prediction and its uncertainty. This approach is explored in [48] to evaluate its feasibility and compared to other alternative methods documented in the literature.…”

Section: Surrogate Modelsmentioning

confidence: 99%

"Review on Surrogate-Based Global Optimization with Biomedical Applications"

Haridy

2023

BJSTR

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Time and accuracy are two key elements that have a significant impact on an algorithm brilliance in many engineering applications. Consequently, many algorithms aim to improve precision and cut down computational costs when resolving contemporary real-life problems. Optimization based on surrogates is considered as an efficient way to keep pace with ever growing problems encountered in modern systems. The design of prosthetic de-vices for people with disabilities involve challenging optimization problems. In such design problems, the relation between the design parameters and the overall performance of the device is complex. Thus, surrogate-based optimization plays an essential role in solving them. This paper reviews the key issues of surrogate-based global optimization starting with the fundamental models used as surrogates, then contemporary research on sampling approaches, and infill criteria with an eye on biomedical applications. Finally, challenges facing surrogate based optimization are discussed.

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Section: Surrogate Modelsmentioning

confidence: 99%

"Review on Surrogate-Based Global Optimization with Biomedical Applications"

Haridy

2023

BJSTR

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Alternative variable-fidelity acquisition functions for efficient global optimization of black-box functions

Ribeiro

Parente

Melo

2023

Struct Multidisc Optim

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System architecture optimization strategies: dealing with expensive hierarchical problems

Bussemaker,

Saves,

Bartoli

et al. 2024

J Glob Optim

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Choosing the right system architecture for the problem at hand is challenging due to the large design space and high uncertainty in the early stage of the design process. Formulating the architecting process as an optimization problem may mitigate some of these challenges. This work investigates strategies for solving system architecture optimization (SAO) problems: expensive, black-box, hierarchical, mixed-discrete, constrained, multi-objective problems that may be subject to hidden constraints. Imputation ratio, correction ratio, correction fraction, and max rate diversity metrics are defined for characterizing hierarchical design spaces. This work considers two classes of optimization algorithms for SAO: multi-objective evolutionary algorithms such as NSGA-II, and Bayesian optimization (BO) algorithms. A new Gaussian process kernel is presented that enables modeling hierarchical categorical variables, extending previous work on modeling continuous and integer hierarchical variables. Next, a hierarchical sampling algorithm that uses design space hierarchy to group design vectors by active design variables is developed. Then, it is demonstrated that integrating more hierarchy information in the optimization algorithms yields better optimization results for BO algorithms. Several realistic single-objective and multi-objective test problems are used for investigations. Finally, the BO algorithm is applied to a jet engine architecture optimization problem. This work shows that the developed BO algorithm can effectively solve the problem with one order of magnitude less function evaluations than NSGA-II. The algorithms and problems used in this work are implemented in the open-source Python library SBArchOpt.

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Surrogate-based optimization based on the probability of feasibility

Cited by 6 publications

References 38 publications

"Review on Surrogate-Based Global Optimization with Biomedical Applications"

"Review on Surrogate-Based Global Optimization with Biomedical Applications"

Alternative variable-fidelity acquisition functions for efficient global optimization of black-box functions

System architecture optimization strategies: dealing with expensive hierarchical problems

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