A multi-fidelity RBF surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems

Yi, Jin; Shen, Yichi; Shoemaker, Christine A.

doi:10.1007/s00158-020-02575-7

Cited by 20 publications

(8 citation statements)

References 60 publications

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“…Simultaneously parametrizing two scale levels that are computationally expensive gave rise to a new multiscale parameter optimization approach. Evidence from related work indicated that surrogate assistance in multiscale optimization is an effective measure to improve the performance of global optimization algorithms, , although it is still outperformed by a space-filling sampling method like LHS. Furthermore, a multistep optimization approach improves the identification of high-quality parameters by presampling candidate solutions that are known to reproduce conformational energies.…”

Section: Discussionmentioning

confidence: 99%

Determining Lennard-Jones Parameters Using Multiscale Target Data through Presampling-Enhanced, Surrogate-Assisted Global Optimization

Müller

Hagg

Strickstrock

et al. 2023

J. Chem. Inf. Model.

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Force field-based models are a Newtonian mechanics approximation of reality and are inherently noisy. Coupling models from different molecular scale domains (including single, gas-phase molecules up to multimolecule, condensed phase ensembles) is difficult, which is also the case for finding solutions that transfer well between the scales. In this contribution, we introduce a surrogate-assisted algorithm to optimize Lennard-Jones parameters for target data from different scale domains to overcome the difficulties named above. Specifically, our approach combines a surrogate-assisted global evolutionary optimization method with a presampling phase that takes advantage of one scale domain being less computationally expensive to evaluate. The algorithm's components were evaluated individually, elucidating their individual merits. Our findings show that the process of parametrizing force fields can significantly benefit from both the presampling method, which alleviates the need to have a good initial guess for the parameters, and the surrogate model, which improves efficiency.

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Section: Discussionmentioning

confidence: 99%

Determining Lennard-Jones Parameters Using Multiscale Target Data through Presampling-Enhanced, Surrogate-Assisted Global Optimization

Müller

Hagg

Strickstrock

et al. 2023

J. Chem. Inf. Model.

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“…Serani et al (2019) investigated the performance of four type of VFSBO strategies based on the stochastic RBF for CFD computer simulations. Yi et al (2020b) presented a multi-fidelity RBF surrogate-based optimization framework, where the LF and HF surrogate models are sequentially exploited. Apart from RBF, support vector regression (SVR) model was also employed to build VF surrogate models (Shi et al 2020a).…”

Section: Related Workmentioning

confidence: 99%

An enhanced variable-fidelity optimization approach for constrained optimization problems and its parallelization

Lin

2022

Struct Multidisc Optim

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In this paper, a variable-fidelity constrained lower confidence bound (VF-CLCB) criterion is presented for computationally expensive constrained optimization problems (COPs) with two levels of fidelity. In VF-CLCB, the hierarchical Kriging model is adopted to model the objective and inequality constraints. Two infill sampling functions are developed based on the objective and the constraints, respectively, and an adaptive selection strategy is set to select the elite sample points. Moreover, based on the VF-CLCB criterion, a parallel optimization method noted as PVF-CLCB is subsequently developed to accelerate the optimization process. In PVF-CLCB, a VF influence function is defined to approximately evaluate the estimation error of the hierarchical Kriging models, based on which multiple promising points can be determined at each iteration. In addition, an allocation strategy is proposed to distribute the computation resources between the objective- and constraint-oriented functions properly. Lastly, the proposed VF-CLCB and PVF-CLCB approaches are compared with the alternative methods on 12 benchmark numerical cases, and their significant superiority in solving computationally expensive COPs is verified. Furthermore, the proposed methods are employed to optimize the global stability of the stiffened cylindrical shell, and the optimum structure is yielded.

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“…In recent years, a number of surrogate-assisted evolutionary algorithms have been successfully applied to solve various optimization problems, such as unconstrained/constrained [119,[123][124][125][126][127][128][129][130], multi-objective [131][132][133][134][135][136][137], and multi-fidelity optimization problems [138,139].…”

Section: Surrogate-assisted Evolutionary Algorithmsmentioning

confidence: 99%

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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A multi-fidelity RBF surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems

Cited by 20 publications

References 60 publications

Determining Lennard-Jones Parameters Using Multiscale Target Data through Presampling-Enhanced, Surrogate-Assisted Global Optimization

Determining Lennard-Jones Parameters Using Multiscale Target Data through Presampling-Enhanced, Surrogate-Assisted Global Optimization

An enhanced variable-fidelity optimization approach for constrained optimization problems and its parallelization

Metamodel-based multidisciplinary design optimization methods for aerospace system

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