Evolutionary Algorithm with Ensemble Classifier Surrogate Model for Expensive Multiobjective Optimization

作     者：LAN Tian 蓝天

作者机构：College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China 

出 版 物：《Transactions of Nanjing University of Aeronautics and Astronautics》 (南京航空航天大学学报（英文版）)

年 卷 期：2020年第37卷第S1期

页      面：76-87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)] 081104[工学-模式识别与智能系统] 08[工学] 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）] 

主　　题：multiobjective evolutionary algorithm expensive multiobjective optimization ensemble classifier surrogate model 

摘      要：For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally *** problems are usually called expensive multiobjective optimization problems(EMOPs).One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function *** from ensemble learning,this paper proposes a multiobjective evolutionary algorithm with an ensemble classifier(MOEA-EC)for *** specifically,multiple decision tree models are used as an ensemble classifier for the pre-selection,which is be more helpful for further reducing the function evaluations of the solutions than using single inaccurate *** extensive experimental studies have been conducted to verify the efficiency of MOEA-EC by comparing it with several advanced multiobjective expensive optimization *** experimental results show that MOEA-EC outperforms the compared algorithms.