摘要
许多实际工程优化问题通常需要同时优化多个相互冲突的目标,并且目标函数的计算主要依赖十分耗时的仿真实验,此类问题一般可称为昂贵多目标优化问题。代理辅助进化算法通过使用机器学习方法建立代理模型,并辅助算法进行评估,因而使代理辅助进化算法成为解决此类问题的热门方法。根据问题规模大小将相关算法划分成两类,描述每类问题特点,分类梳理相关算法,并说明每个算法的优缺点,以便人们能直观地了解连续昂贵多目标优化问题研究进展,更好地开展后续研究工作。
Many practical engineering optimization problems usually involve optimizing multiple conflicting objectives at the same time,and the calculation of the objective function mainly relies on time-consuming simulation experiments,such problems can generally be called expensive multiobjective optimization problems.Surrogate-assisted evolutionary algorithms use machine learning methods to build surrogate models and assist algorithms for evaluation,which makes surrogate-assisted evolutionary algorithms a popular method to solve such problems.According to the scale of the problem,the relevant algorithms are divided into two categories,the characteristics of each type of problem are described,the related algorithms are classified and sorted out,and the advantages and disadvantages of each algorithm are explained,so that people can intuitively understand the research progress of continuous expensive multiobjective optimization problems and better carry out follow-up research work.
作者
张峰
陈新中
ZHANG Feng;CHEN Xin-zhong(The 28th Research Institute of China Electronics Technology Group Corporation,Nanjing 210007,China)
出处
《软件导刊》
2023年第5期248-252,共5页
Software Guide
关键词
多目标优化
昂贵多目标优化
代理辅助进化算法
代理模型
机器学习
multiobjective optimization
expensive multiobjective optimization
surrogate-assisted evolutionary algorithm
surrogate model
machine learning
