<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Efficient Global Optimization (EGO) algorithm with Kriging model is stable and effective for an expensive black-box function. However, How to get a more global optimal point on the basis of surrogates has been concerned in simulation-based design optimization. In order to better solve a black-box unconstrained optimization problem, this paper introduces a new EGO method named improved generalized EGO (IGEGO), in which two targets will be achieved: using Kriging surrogate model and guiding the optimal searching direction into more promising regions. Kriging modeling which can fast construct an approximation model is the premise ofperforming optimization. Next, a new infill sampling criterion (ISC) called improved generalized expected improvement which round off Euclidean norm on variation of the optimal solutions ofparameter θ to replace parameter g can effectively balance global and local search in IGEGO method. Twelve numerical tests and an engineering example are given to illustrate the reliability, applicability and effectiveness of the present method</p>
</abstract>

Efficient Global Optimization (EGO) algorithm with Kriging model is stable and effective for an expensive black-box function. However, How to get a more global optimal point on the basis of surrogates has been concerned in simulation-based design optimization. In order to better solve a black-box unconstrained optimization problem, this paper introduces a new EGO method named improved generalized EGO (IGEGO), in which two targets will be achieved: using Kriging surrogate model and guiding the optimal searching direction into more promising regions. Kriging modeling which can fast construct an approximation model is the premise ofperforming optimization. Next, a new infill sampling criterion (ISC) called improved generalized expected improvement which round off Euclidean norm on variation of the optimal solutions ofparameter θ to replace parameter g can effectively balance global and local search in IGEGO method. Twelve numerical tests and an engineering example are given to illustrate the reliability, applicability and effectiveness of the present method

A Kriging-Based Unconstrained Global Optimization Algorithm

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A Kriging-Based Unconstrained Global Optimization Algorithm"}

Efficient Global Optimization (EGO) algorithm with Kriging model is stable and effective for an expensive black-box function. However, How to get a more global...

A Kriging-Based Unconstrained Global Optimization Algorithm

Data publikacji: 01 cze 2016

Zakres stron: 927 - 952

Otrzymano: 10 sty 2016

Przyjęty: 31 mar 2016

DOI: https://doi.org/10.21307/ijssis-2017-902

Słowa kluczowe
Global optimization, Black-box function, Efficient Global Optimization (EGO), Kriging model, Infill sampling criterion

© 2016 Yaohui Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Kriging-Based Unconstrained Global Optimization Algorithm

Data publikacji: 01 cze 2016

Zakres stron: 927 - 952

Otrzymano: 10 sty 2016

Przyjęty: 31 mar 2016

DOI: https://doi.org/10.21307/ijssis-2017-902

Słowa kluczoweGlobal optimization, Black-box function, Efficient Global Optimization (EGO), Kriging model, Infill sampling criterion

© 2016 Yaohui Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Global optimization, Black-box function, Efficient Global Optimization (EGO), Kriging model, Infill sampling criterion