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Evolutionary Intelligence
Erschienen in: Evolutionary Intelligence 4/2022

23.01.2020 | Special Issue

A Kriging-based multi-point sequential sampling optimization method for complex black-box problem

verfasst von: Yaohui Li

Erschienen in: Evolutionary Intelligence | Ausgabe 4/2022

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Abstract

The Generalized Efficient Global Optimization (GEGO) algorithm assisted by Kriging model can solve black-box problem of complex computing. However, a single sampling point obtained in each iteration process may cause longer objective-evaluation time and slower convergence speed in contrast with multi-point sampling optimization methods. For this, a Kriging-based multi-point sequential sampling optimization (KMSSO) method is presented. The proposed method uses uncertainty estimate information of Kriging to construct the multiple-point generalized Expected Improvement (EI) criterion. In optimization cycle, this criterion is maximized to produce the Pareto front data, which will be further screened to obtain final expensive evaluation points. For numerical tests and an engineering case, KMSSO is compared to GEGO and HAM algorithm and is shown to deliver better results. It is also proves that when multiple points are added per cycle, the optimization accuracy and convergence property are both improved.
Vorheriger Artikel Use a sequential gradient-enhanced-Kriging optimal experimental design method to build high-precision approximate model for complex simulation problem
Nächster Artikel RETRACTED ARTICLE: Chatbot design issues: building intelligence with the Cartesian paradigm

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Metadaten
Titel
A Kriging-based multi-point sequential sampling optimization method for complex black-box problem
verfasst von
Yaohui Li
Publikationsdatum
23.01.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Evolutionary Intelligence / Ausgabe 4/2022
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-020-00352-5

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