Surrogate-assisted evolutionary algorithm with hierarchical surrogate technique and adaptive infill strategy

Chen, Hao; Li, Weikun; Cui, Weicheng

doi:10.1016/j.eswa.2023.120826

Cited by 8 publications

(1 citation statement)

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“…Zhang and Ni (2023) proposed a new framework for constructing the sparse polynomial chaos expansion (PCE) model adaptively, which allows for the consistent selection of sampling points and basis functions. Chen et al (2023) introduced a surrogate-assisted evolutionary algorithm based on high-dimensional model representation (HDMR) and the adaptive sampling technique. Li and Marzouk (2014) developed a polynomial chaos model and achieved adaptation by adding new sample points with high posterior probability.…”

Section: Introductionmentioning

confidence: 99%

An adaptive multi-output Gaussian process surrogate model for large-scale parameter estimation problems

Lyu,

Huang,

et al. 2024

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PurposeParameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper aims to introduce an adaptive multi-output Gaussian process (MOGP) surrogate model for parameter estimation in time-consuming models.Design/methodology/approachThe MOGP surrogate model is established to replace the computationally expensive finite element method (FEM) analysis during the estimation process. We propose a novel adaptive sampling method for MOGP inspired by the traditional expected improvement (EI) method, aiming to reduce the number of required sample points for building the surrogate model. Two mathematical examples and an application in the back analysis of a concrete arch dam are tested to demonstrate the effectiveness of the proposed method.FindingsThe numerical results show that the proposed method requires a relatively small number of sample points to achieve accurate estimates. The proposed adaptive sampling method combined with the MOGP surrogate model shows an obvious advantage in parameter estimation problems involving expensive-to-evaluate models, particularly those with high-dimensional output.Originality/valueA novel adaptive sampling method for establishing the MOGP surrogate model is proposed to accelerate the procedure of solving large-scale parameter estimation problems. This modified adaptive sampling method, based on the traditional EI method, is better suited for multi-output problems, making it highly valuable for numerous practical engineering applications.

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Section: Introductionmentioning

confidence: 99%