期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 7, 页码 5720-5731出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3061420
关键词
Optimization; Computational modeling; Mathematical model; Databases; Data models; Search problems; Predictive models; Expensive optimization; exploration and exploitation; radial basis function model (RBF)
类别
资金
- National Natural Science Foundation of China [61876163]
This article presents a three-level radial basis function-assisted optimization algorithm for expensive optimization, which performs well in various tests by conducting global exploration, subregion search, and local exploitation search processes.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据