Filter-based adaptive Kriging method for black-box optimization problems with expensive objective and constraints

Shi, Renhe; Liu, Li; Li, Teng; Wu, Yufei; Tang, Yifan

doi:10.1016/j.cma.2018.12.026

Cited by 43 publications

(10 citation statements)

References 32 publications

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“…To further improve the optimization capacity, some variants of efficient global optimization have been developed in recent years, especially in constraint handling [72], parallel infill sampling [73], and highdimensional optimization [74]. Further details of the variants of efficient global optimization are discussed in Refs.…”

Section: Efficient Global Optimization Methodsmentioning

confidence: 99%

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Metamodel-based multidisciplinary design optimization methods for aerospace system

Shi

et al. 2021

Astrodyn

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The design of complex aerospace systems is a multidisciplinary design optimization (MDO) problem involving the interaction of multiple disciplines. However, because of the necessity of evaluating expensive black-box simulations, the enormous computational cost of solving MDO problems in aerospace systems has also become a problem in practice. To resolve this, metamodel-based design optimization techniques have been applied to MDO. With these methods, system models can be rapidly predicted using approximate metamodels to improve the optimization efficiency. This paper presents an overall survey of metamodel-based MDO for aerospace systems. From the perspective of aerospace system design, this paper introduces the fundamental methodology and technology of metamodel-based MDO, including aerospace system MDO problem formulation, metamodeling techniques, state-of-the-art metamodel-based multidisciplinary optimization strategies, and expensive black-box constraint-handling mechanisms. Moreover, various aerospace system examples are presented to illustrate the application of metamodel-based MDOs to practical engineering. The conclusions derived from this work are summarized in the final section of the paper. The survey results are expected to serve as guide and reference for designers involved in metamodel-based MDO in the field of aerospace engineering.

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Section: Efficient Global Optimization Methodsmentioning

confidence: 99%

“…In Ref. [72], the probability of constrained improvement is enhanced; only the sample point with a positive probability of constrained improvement can be accepted by the filter to enhance optimality and feasibility.…”

Section: Region For Refining Filte Rmentioning

confidence: 99%

Metamodel-based multidisciplinary design optimization methods for aerospace system

Shi

et al. 2021

Astrodyn

Self Cite

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“…Since the initial development of EGO, multiple infill criteria have been developed, such as maximizing the probability of improvement and minimizing the lower confidence bounding [463][464][465][466]. Liu et al [464] showed that using these criteria in parallel is effective in improving the optimization efficiency.…”

Section: Surrogate-based Optimizationmentioning

confidence: 99%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

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Large volumes of experimental and simulation aerodynamic data have been rapidly advancing aerodynamic shape optimization (ASO) via machine learning (ML), whose effectiveness has been growing thanks to continued developments in deep learning. In this review, we first introduce the state of the art and the unsolved challenges in ASO. Next, we present a description of ML fundamentals and detail the ML algorithms that have succeeded in ASO. Then we review ML applications contributing to ASO from three fundamental perspectives: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands like interactive design optimization. However, practical large-scale design optimizations remain a challenge due to the costly ML training expense. A deep coupling of ML model construction with ASO prior experience and knowledge, such as taking physics into account, is recommended to train ML models effectively.

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“…In the last decade, kriging-based methods [23,24,25,26] and Bayesian optimization (BO) [27,28] grew in popularity. These techniques have a common base: fitting Gaussian process (GP) priors to existing objective and constraint data, and selecting a point that maximizes an acquisition function over the fitted prior.…”

Section: Previous Workmentioning

confidence: 99%

SMGO-$Δ$: Balancing Caution and Reward in Global Optimization with Black-Box Constraints

Sabug¹,

Ruiz²,

Fagiano³

2022

Preprint

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In numerous applications across all science and engineering areas, there are optimization problems where both the objective function and the constraints have no closed-form expression or are too complex to be managed analytically, that they can only be evaluated through experiments. To address such issues, we design a global optimization technique for problems with black-box objective and constraints. Assuming Lipschitz continuity of the cost and constraint functions, a Set Membership framework is adopted to build a surrogate model of the optimization program, that is used for exploitation and exploration routines. The resulting algorithm, named Set Membership Global Optimization With Black-Box Constraints (SMGO-∆), features one tunable risk parameter, which the user can intuitively adjust to trade-off safety, exploitation, and exploration. The theoretical properties of the algorithm are derived, and the optimization performance is compared with representative techniques from the literature in several benchmarks. Lastly, it is tested and compared with constrained Bayesian optimization in a case study pertaining to model predictive control tuning for a servomechanism with disturbances and plant uncertainties, * Corresponding author. The first author would like to acknowledge the support of the Department of Science and Technology-Science Education Institute (DOST-SEI) of the Philippines for his research.

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Filter-based adaptive Kriging method for black-box optimization problems with expensive objective and constraints

Cited by 43 publications

References 32 publications

Metamodel-based multidisciplinary design optimization methods for aerospace system

Metamodel-based multidisciplinary design optimization methods for aerospace system

Machine Learning in Aerodynamic Shape Optimization

SMGO-$Δ$: Balancing Caution and Reward in Global Optimization with Black-Box Constraints

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