Details
| Original language | English |
|---|---|
| Article number | 103178 |
| Journal | Probabilistic Engineering Mechanics |
| Volume | 67 |
| Early online date | 8 Nov 2021 |
| Publication status | Published - Jan 2022 |
Abstract
This paper presents a Markov sampling-based framework, called Asymptotic Bayesian Optimization, for solving a class of constrained design optimization problems. The optimization problem is converted into a unified two-phase sample generation problem which is solved by an effective Markov chain Monte Carlo simulation scheme. First, an exploration phase generates designs distributed over the feasible design space. Based on this information, an exploitation phase obtains a set of designs lying in the vicinity of the optimal solution set. The proposed formulation can handle continuous, discrete, or mixed discrete-continuous design variables. Appropriate adaptive proposal distributions for the continuous and discrete design variables are suggested. The set of optimal solutions provides valuable sensitivity information of the different quantities involved in the problem with respect to the design variables. Representative examples including an analytical problem involving nonlinear benchmark functions, a classical engineering design problem, and a performance-based design optimization problem of a structural system under stochastic excitation are presented to show the effectiveness and potentiality of the proposed optimization scheme. Validation calculations show that the scheme is a flexible, efficient and competitive choice for solving a wide range of classical and complex engineering design problems.
Keywords
- Discrete-continuous optimization, Dynamic systems, Markov sampling method, Metropolis–Hastings algorithm, Performance-based design, Proposal distributions, Stochastic optimization
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Statistical and Nonlinear Physics
- Engineering(all)
- Civil and Structural Engineering
- Energy(all)
- Nuclear Energy and Engineering
- Engineering(all)
- Aerospace Engineering
- Physics and Astronomy(all)
- Condensed Matter Physics
- Engineering(all)
- Ocean Engineering
- Engineering(all)
- Mechanical Engineering
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In: Probabilistic Engineering Mechanics, Vol. 67, 103178, 01.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Asymptotic Bayesian Optimization
T2 - A Markov sampling-based framework for design optimization
AU - Jerez, D. J.
AU - Jensen, H. A.
AU - Beer, M.
AU - Chen, J.
N1 - Funding Information: The research reported here was supported in part by ANID (National Agency for Research and Development, Chile) under grant number 1200087 . Also, this research has been supported by ANID, Chile and DAAD (German Academic Exchange Service) under CONICYT-PFCHA/ Doctorado Acuerdo Bilateral DAAD Becas Chile/2018-62180007. These supports are gratefully acknowledged by the authors.
PY - 2022/1
Y1 - 2022/1
N2 - This paper presents a Markov sampling-based framework, called Asymptotic Bayesian Optimization, for solving a class of constrained design optimization problems. The optimization problem is converted into a unified two-phase sample generation problem which is solved by an effective Markov chain Monte Carlo simulation scheme. First, an exploration phase generates designs distributed over the feasible design space. Based on this information, an exploitation phase obtains a set of designs lying in the vicinity of the optimal solution set. The proposed formulation can handle continuous, discrete, or mixed discrete-continuous design variables. Appropriate adaptive proposal distributions for the continuous and discrete design variables are suggested. The set of optimal solutions provides valuable sensitivity information of the different quantities involved in the problem with respect to the design variables. Representative examples including an analytical problem involving nonlinear benchmark functions, a classical engineering design problem, and a performance-based design optimization problem of a structural system under stochastic excitation are presented to show the effectiveness and potentiality of the proposed optimization scheme. Validation calculations show that the scheme is a flexible, efficient and competitive choice for solving a wide range of classical and complex engineering design problems.
AB - This paper presents a Markov sampling-based framework, called Asymptotic Bayesian Optimization, for solving a class of constrained design optimization problems. The optimization problem is converted into a unified two-phase sample generation problem which is solved by an effective Markov chain Monte Carlo simulation scheme. First, an exploration phase generates designs distributed over the feasible design space. Based on this information, an exploitation phase obtains a set of designs lying in the vicinity of the optimal solution set. The proposed formulation can handle continuous, discrete, or mixed discrete-continuous design variables. Appropriate adaptive proposal distributions for the continuous and discrete design variables are suggested. The set of optimal solutions provides valuable sensitivity information of the different quantities involved in the problem with respect to the design variables. Representative examples including an analytical problem involving nonlinear benchmark functions, a classical engineering design problem, and a performance-based design optimization problem of a structural system under stochastic excitation are presented to show the effectiveness and potentiality of the proposed optimization scheme. Validation calculations show that the scheme is a flexible, efficient and competitive choice for solving a wide range of classical and complex engineering design problems.
KW - Discrete-continuous optimization
KW - Dynamic systems
KW - Markov sampling method
KW - Metropolis–Hastings algorithm
KW - Performance-based design
KW - Proposal distributions
KW - Stochastic optimization
UR - http://www.scopus.com/inward/record.url?scp=85119177180&partnerID=8YFLogxK
U2 - 10.1016/j.probengmech.2021.103178
DO - 10.1016/j.probengmech.2021.103178
M3 - Article
AN - SCOPUS:85119177180
VL - 67
JO - Probabilistic Engineering Mechanics
JF - Probabilistic Engineering Mechanics
SN - 0266-8920
M1 - 103178
ER -