An aRBF surrogate-assisted neighborhood field optimizer for expensive problems

Abstract Surrogate-assisted evolutionary algorithms (SAEAs) have recently received increasing attention in solving computationally expensive engineering optimization problems. Existing studies have shown that surrogate modeling techniques based on different radial basis functions (RBF) can highly affect the search capability of an optimizer. However, without any prior knowledge about the optimization problem to be solved, it is very hard for a designer to decide which modeling techniques should be used. To defeat this issue, we suggested a brand-new model management strategy based on multi-RBF parallel modeling technology in this paper. The proposed strategy aims to adaptively select a high-fidelity surrogate from a pre-specified set of RBF modeling techniques during the optimization process. At each evolutionary interaction, the most promising RBF surrogate was employed to help neighborhood field optimizer (NFO) perform fitness evaluation, and the proposed algorithm is named aRBF-NFO. Moreover, a detailed experimental analysis was given to show the effectiveness of the proposed method, and an overall comparison was made between the aRBF-NFO and two state-of-the-art SAEAs on a commonly-used test set as well as an antenna optimization problem. Experimental results demonstrate the proposed algorithm is robust and efficient.