Large-Scale Expensive Optimization with a Switching Strategy

作     者：Mai Sun Chaoli Sun Xiaobo Li Guochen Zhang Farooq Akhtar 

作者机构：School of Computer Science and TechnologyTaiyuan University of Science and TechnologyTaiyuan 030024China Department of Computer Sciences and Information TechnologyUniversity of Kotli Azad Jammu and KashmirKotli 11100Pakistan 

出 版 物：《Complex System Modeling and Simulation》 (复杂系统建模与仿真（英文）)

年 卷 期：2022年第2卷第3期

页      面：253-263页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported in part by the National Natural Science Foundation of China(No.61876123) Shanxi Key Research and Development Program(No.202102020101002) Natural Science Foundation of Shanxi Province(Nos.201901D111264 and 201901D111262) 

主　　题：large-scale optimization problems computationally expensive problems random grouping surrogate models 

摘      要：Some optimization problems in scientific research,such as the robustness optimization for the Internet of Things and the neural architecture search,are large-scale in decision space and expensive for objective *** order to get a good solution in a limited budget for the large-scale expensive optimization,a random grouping strategy is adopted to divide the problem into some low-dimensional sub-problems.A surrogate model is then trained for each sub-problem using different strategies to select training data *** that,a dynamic infill criterion is proposed corresponding to the models currently used in the surrogate-assisted sub-problem ***,an escape mechanism is proposed to keep the diversity of the *** performance of the method is evaluated on CEC’2013 benchmark *** results show that the algorithm has better performance in solving expensive large-scale optimization problems.