A Three-Level Radial Basis Function Method for Expensive Optimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)5720-5731
Journal / PublicationIEEE Transactions on Cybernetics
Volume52
Issue number7
Online published22 Mar 2021
Publication statusPublished - Jul 2022

Abstract

This article proposes a three-level radial basis function (TLRBF)-assisted optimization algorithm for expensive optimization. It consists of three search procedures at each iteration: 1) the global exploration search is to find a solution by optimizing a global RBF approximation function subject to a distance constraint in the whole search space; 2) the subregion search is to generate a solution by minimizing an RBF approximation function in a subregion determined by fuzzy clustering; and 3) the local exploitation search is to generate a solution by solving a local RBF approximation model in the neighborhood of the current best solution. Compared with some other state-of-the-art algorithms on five commonly used scalable benchmark problems, ten CEC2015 computationally expensive problems, and a real-world airfoil design optimization problem, our proposed algorithm performs well for expensive optimization.

Research Area(s)

  • Computational modeling, Data models, Databases, Expensive optimization, exploration and exploitation, Mathematical model, Optimization, Predictive models, radial basis function model (RBF)., Search problems

Citation Format(s)

A Three-Level Radial Basis Function Method for Expensive Optimization. / Li, Genghui; Zhang, Qingfu; Lin, Qiuzhen et al.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 7, 07.2022, p. 5720-5731.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review