文献类型：期刊文献

英文题名：A surrogate-assisted evolutionary algorithm for expensive many-objective optimization in the refining process

作者：Han, Dong[1];Du, Wenli[1];Wang, Xinjie[1];Du, Wei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2022

卷号：69

外文期刊名：SWARM AND EVOLUTIONARY COMPUTATION

收录：;EI(收录号：20214211037135);WOS:【SCI-EXPANDED(收录号:WOS:000782991200008)】；

基金：ThisworkwassupportedbyNationalNaturalScienceFoundationofChina(MajorProgram:61590923),NationalScienceFundforDistin-guishedYoungScholars(61725301),theFundamentalResearchFundsfortheCentralUniversitiesandtheProgrammeofIntroducingTalentsofDisciplinetoUniversities(the111Project)underGrantB17017

语种：英文

外文关键词：Surrogate-assisted evolutionary optimization; Expensive many-objective optimization; Model management; Decomposition; Engineering optimization

摘要：Computationally expensive optimization problems are difficult for an evolutionary algorithm within limited fitness evaluations, especially for many-objective optimization. To remedy this issue, a surrogate-assisted decomposition-based evolutionary algorithm is proposed in this work where the Kriging model is used to approximate each objective function. In order to balance exploration and exploitation, the model management strategy integrates the infill criterion with the distribution information of both weight vectors and population. Additionally, a one-by-one replacement strategy is developed to select a fixed number of training data to limit computational time for the model training as well as maintain the approximation accuracy. The empirical results show that the performance of the proposed algorithm is competitive to the state-of-the-art algorithms on a number of benchmarks. Finally, the proposed algorithm is applied for a real-world optimization problem in the refining process and exhibits superior performance.