Data-driven evolutionary sampling optimization for expensive problems

作     者：ZHEN Huixiang GONG Wenyin WANG Ling ZHEN Huixiang;GONG Wenyin;WANG Ling

作者机构：School of Computer ScienceChina University of GeosciencesWuhan 430074China Department of AutomationTsinghua UniversityBeijing 100084China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2021年第32卷第2期

页      面：318-330页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 081104[工学-模式识别与智能系统] 08[工学] 0802[工学-机械工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(62076225,62073300) the Natural Science Foundation for Distinguished Young Scholars of Hubei(2019CFA081) the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(CUGGC03) 

主　　题：evolutionary algorithm(EA) surrogate model datadriven evolutionary sampling airfoil design 

摘      要：Surrogate models have shown to be effective in assisting evolutionary algorithms(EAs)for solving computationally expensive complex optimization ***,the effectiveness of the existing surrogate-assisted evolutionary algorithms still needs to be improved.A data-driven evolutionary sampling optimization(DESO)framework is proposed,where at each generation it randomly employs one of two evolutionary sampling strategies,surrogate screening and surrogate local search based on historical data,to effectively balance global and local *** DESO,the radial basis function(RBF)is used as the surrogate model in the sampling strategy,and different degrees of the evolutionary process are used to sample candidate *** sampled points by sampling strategies are evaluated,and then added into the database for the updating surrogate model and population in the next *** get the insight of DESO,extensive experiments and analysis of DESO have been *** proposed algorithm presents superior computational efficiency and robustness compared with five state-of-the-art algorithms on benchmark problems from 20 to 200 ***,DESO is applied to an airfoil design problem to show its effectiveness.