Alswaitti, M, Siddique, K, Jiang, S, Alomoush, W and Alrosan, A (2022) Dimensionality Reduction, Modelling, and Optimization of Multivariate Problems Based on Machine Learning. Symmetry.
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Abstract
Simulation-based optimization design is becoming increasingly important in engineering. However, carrying out multi-point, multi-variable, and multi-objective optimization work is faced with the “Curse of Dimensionality”, which is highly time-consuming and often limited by computational burdens as in aerodynamic optimization problems. In this paper, an active subspace dimensionality reduction method and the adaptive surrogate model were proposed to reduce such computational costs while keeping a high precision. In this method, the active subspace dimensionality reduction technique, three-layer radial basis neural network approach, and polynomial fitting process were presented. For the model evaluation, a NASA standard test function problem and RAE2822 airfoil drag reduction optimization were investigated in the experimental design problem. The efficacy of the method was proved by both the experimental examples in which the adaptive surrogate model in a dominant one-dimensional active subspace is given and the optimization efficiency was improved by two orders. Furthermore, the results show that the constructed surrogate model reduced dimensionality and alleviated the complexity of conventional multivariate surrogate modeling with high precision.
Affiliation: | Skyline University College |
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SUC Author(s): | Alomoush, W ORCID: https://orcid.org/0000-0002-2937-4327 and Alrosan, A ORCID: https://orcid.org/0000-0001-9400-4077 |
All Author(s): | Alswaitti, M, Siddique, K, Jiang, S, Alomoush, W and Alrosan, A |
Item Type: | Article |
Uncontrolled Keywords: | active subspace; dimensionality reduction; surrogate model; optimization design; multivariate problems |
Subjects: | B Information Technology > BL Machine Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Veeramani Rasu |
Date Deposited: | 22 Jun 2022 14:22 |
Last Modified: | 22 Jun 2022 14:22 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/467 |
Publisher URL: | https://doi.org/10.3390/sym14071282 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/17526 |
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