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A Three-Level Radial Basis Function Method for Expensive Optimization
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DOI：10.1109/TCYB.2021.3061420.png)
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Abstract
En 中文
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.
Keywords:
Optimization
Computational modeling
Mathematical model
Databases
Data models
Search problems
Predictive models
Expensive optimization
exploration and exploitation
radial basis function model (RBF)
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