A Three-Level Radial Basis Function Method for Expensive Optimization

Authors：Li, Genghui^*; Zhang, Qingfu; Lin, Qiuzhen; Gao, Weifeng

Source：IEEE Transactions on Cybernetics, 2022, 52(7): 5720-5731.

DOI：10.1109/TCYB.2021.3061420

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

This article proposes a three-level radial basis 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.

• Institution
  西安电子科技大学