This paper proposes a kind of Global Sequential Sampling Algorithm (GSSA) based on surrogate model. With the process of iteration, GSSA can sample both in ...

Sep 4, 2014 · In this paper, two new adaptive sampling algorithms – one purely adaptive and one combining adaptive and space-filling characteristics – are proposed.

Dec 18, 2023 · The proposed sequential sampling method is compared with four state-of-the-art sequential sampling methods for creating Kriging surrogate models ...

The local properties of these functions in the spaces Lp on the graphs that defined in terms of the vertices of the graph are the same as for the interval case.

Apr 27, 2023 · In this work, a scalable algorithm is developed for EGO using NN-based prediction and uncertainty (EGONN).

Oct 3, 2023 · Surrogate models based on machine learning methods have become an important part of modern engineering to replace costly computer simulations.

Surrogate-based global optimization algorithms use a surrogate model along with a sampling criterion. The AMP-SBGO algorithm sequentially samples points to ...

We propose a new sequential sampling strategy based on error filtering and distance clustering to construct and update surrogate models.

SSS method is developed to generated sequential samples towards the global optimum for updating RBF surrogate. (2) For constrained optimization problems ...

Jul 2, 2020 · (SMISA) A Sequential Multi-point Infill Sampling Algorithm (SMISA) is any algorithm of the parallel surrogate-based optimization framework given ...