Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A SurveyOACSTPCDEI

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization problems.The past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive prob-lems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer simulations.Moreover,it is hard to tra-verse the huge search space within reasonable resource as prob-lem dimension increases.Traditional evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satis-factory results.To reduce such evaluations,many novel surro-gate-assisted algorithms emerge to cope with HEPs in recent years.Yet there lacks a thorough review of the state of the art in this specific and important area.This paper provides a compre-hensive survey of these evolutionary algorithms for HEPs.We start with a brief introduction to the research status and the basic concepts of HEPs.Then,we present surrogate-assisted evolution-ary algorithms for HEPs from four main aspects.We also give comparative results of some representative algorithms and appli-cation examples.Finally,we indicate open challenges and several promising directions to advance the progress in evolutionary opti-mization algorithms for HEPs.