A Joint Optimization Algorithm Based on the Optimal Shape Parameter–Gaussian Radial Basis Function Surrogate Model and Its Application

Jian Sun, Ling Wang and Dianxuan Gong ()
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Jian Sun: College of Science, North China University of Science and Technology, Tangshan 063210, China
Ling Wang: College of Science, North China University of Science and Technology, Tangshan 063210, China
Dianxuan Gong: College of Science, North China University of Science and Technology, Tangshan 063210, China

Mathematics, 2023, vol. 11, issue 14, 1-20

Abstract: We propose a joint optimization algorithm that combines the optimal shape parameter–Gaussian radial basis function (G-RBF) surrogate model with global and local optimization techniques to improve accuracy and reduce costs. We analyze factors that affect the accuracy of the G-RBF surrogate model and use the particle swarm optimization (PSO) algorithm to determine the optimal shape parameter and control the number and spacing of the sampling points for a high-precision surrogate model. Global optimization refines the surrogate model, serving as the initial value for local optimization to further refine the problem. Our experiments show that this method significantly reduces computation costs. We optimize the section size of cantilever beams for different materials, obtaining the optimal section size and mass for each. We find that hard aluminum alloy is the optimal choice, meeting yield strength and deflection requirements through finite element analysis verification. Our work highlights the effectiveness of the joint optimization algorithm based on the surrogate model, providing valuable tools and insights into optimizing various structures.

Keywords: joint optimization; G-RBF; surrogate model; shape parameter; PSO; cantilever beam (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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