A Comparative Study on Bayesian Optimization

Constraint Handling in Bayesian Optimization -- A Comparative Study of Support Vector Machine, Augmented Lagrangian and Expected Feasible Improvement

10.1115/gt2021-58562 ◽

2021 ◽

Author(s):

Yuan Jin ◽

Zheyi Yang ◽

Shiran Dai ◽

Yann Lebret ◽

Olivier Jung

Keyword(s):

Support Vector Machine ◽

Comparative Study ◽

Constrained Optimization ◽

Augmented Lagrangian ◽

Optimization Problems ◽

Bayesian Optimization ◽

Constraint Handling ◽

Sequential Optimization ◽

Support Vector ◽

Complex Constraints

Abstract Many engineering problems involve complex constraints which can be computationally costly. To reduce the overall numerical cost, such constrained optimization problems are solved via surrogate models constructed on a Design of Experiment (DoE). Meanwhile, complex constraints may lead to infeasible initial DoE, which can be problematic for subsequent sequential optimization. In this study, we address constrained optimization problem in a Bayesian optimization framework. A comparative study is conducted to evaluate the performance of three approaches namely Expected Feasible Improvement (EFI) and slack Augmented Lagrangian method (AL) and Expected Improvement with Probabilistic Support Vector Machine in constraint handling with feasible or infeasible initial DoE. AL is capable to start sequential optimization with infeasible initial DoE, while EFI requires extra a priori enrichment to find at least one feasible sample. Empirical experiments are performed on both analytical functions and a low pressure turbine disc design problem. Through these benchmark problems, EFI and AL are shown to have overall similar performance in problems with inequality constraints. However, the performance of EIPSVM is affected strongly by the corresponding hyperparameter values. In addition, we show evidences that with an appropriate handling of infeasible initial DoE, EFI does not necessarily underperform compared with AL solving optimization problems with mixed inequality and equality constraints.

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Optimized Gaussian Process Regression by Bayesian Optimization to Forecast COVID-19 Spread in India and Brazil: A Comparative Study

10.1109/iciss53185.2021.9532501 ◽

2021 ◽

Author(s):

Yasminah Alali ◽

Fouzi Harrou ◽

Ying Sun

Keyword(s):

Comparative Study ◽

Gaussian Process ◽

Gaussian Process Regression ◽

Bayesian Optimization

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Evaluation of Neural Architecture Search Approaches for Offshore Platform Offset Prediction

10.5753/eniac.2021.18264 ◽

2021 ◽

Author(s):

Tomaz M. Suller ◽

Eric O. Gomes ◽

Henrique B. Oliveira ◽

Lucas P. Cotrim ◽

Amir M. Sa’ad ◽

...

Keyword(s):

Simulated Annealing ◽

Comparative Study ◽

Random Search ◽

Center Of Gravity ◽

Offshore Platform ◽

Bayesian Optimization ◽

Optimization Process ◽

Search Process ◽

Multi Layer Perceptron ◽

Neural Architecture

This paper proposes a solution based on Multi-Layer Perceptron (MLP) to predict the offset of the center of gravity of an offshore platform. It also performs a comparative study with three optimization algorithms – Random Search, Simulated Annealing, and Bayesian Optimization (BO) – to find the best MLP architecture. Although BO obtained the best architecture in the shortest time, ablation studies developed in this paper with hyperparameters of the optimization process showed that the result is sensitive to them and deserves attention in the Neural Architecture Search process.

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