Full long-term extreme buffeting response calculations using sequential Gaussian process surrogate modeling

Lystad, Tor M.; Fenerci, Aksel; Øiseth, Ole

doi:10.1016/j.engstruct.2023.116495

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…On the other hand, the local surrogate model is utilized to enhance the approximation accuracy within a confined search space, such as Kriging model (Jeong et al, 2005), locally weighted regression (Talgorn et al, 2018), and support vector machine regression (SVM) (Ciccazzo et al, 2015), etc. Compared to methods like artificial neural network (ANN) models, surrogate models based on the Bayesian framework have been widely adopted due to their high model fitting accuracy and the ability to provide approximate values of uncertainty, which proves highly effective in model management (Lystad et al, 2023). Among these, surrogate optimization based on the Kriging model and GP model are typical examples of Bayesianbased surrogate optimization methods (Liu J. et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

An effective surrogate model assisted algorithm for multi-objective optimization: application to wind farm layout design

Chen,

Wang,

Huang

2023

Front. Energy Res.

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Due to the intricate and diverse nature of industrial systems, traditional optimization algorithms require a significant amount of time to search for the optimal solution throughout the entire design space, making them unsuitable for meeting practical industrial demands. To address this issue, we propose a novel approach that combines surrogate models with optimization algorithms. Firstly, we introduce the Sparse Gaussian Process regression (SGP) into the surrogate model, proposing the SGP surrogate-assisted optimization method. This approach effectively overcomes the computational expense caused by the large amount of data required in Gaussian Process model. Secondly, we use grid partitioning to divide the optimization problem into multiple regions, and utilize the multi-objective particle swarm optimization algorithm to optimize particles in each region. By combining the advantages of grid partitioning and particle swarm optimization, which overcome the limitations of traditional optimization algorithms in handling multi-objective problems. Lastly, the effectiveness and robustness of the proposed method are verified through three types of 12 test functions and a wind farm layout optimization case study. The results show that the combination of meshing and SGP surrogate enables more accurate identification of optimal solutions, thereby improving the accuracy and speed of the optimization results. Additionally, the method demonstrates its applicability to a variety of complex multi-objective optimization problems.

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