摘要
提出一种基于R2指标的昂贵多目标进化(R2-EMO)算法.为了解决精确数学模型难以获得以及评估实验成本高昂的昂贵多目标优化问题,R2-EMO算法采用高斯过程取代真实模型来评估个体在每个目标上的性能,并设计一种新的R2指标的效用函数,该效用函数根据高斯过程的输出计算个体的R2指标.带有新的效用函数的R2指标在选择评估点时,既考虑了种群个体的收敛性和多样性,还考虑了个体的预测期望值和预测均方误差,增强了种群个体对目标空间的勘探能力.同时,提出双层档案管理策略,采用两个档案分别存放评估过的非支配个体和建立代理模型的个体,并在每次迭代中对两个档案进行更新.实验结果表明,与已有的4种算法相比,R2-EMO算法在处理昂贵多目标进化算法时,收敛性和多样性均优于其他算法,并能以较快的速度收敛到Pareto前沿.
In order to solve the expensive multi-objective optimization problems,whose accurate mathematical models are difficult to obtain and which have high experimental cost in evaluation experiments,an expensive multi-objective evolutionary algorithm based on the R2 indicator(R2-EMO)is proposed,which uses the Gaussian process to replace the real model to evaluate the performance of the individual by calculating the R2 indicator.In selecting the evaluation points,the R2 indicator considers the convergence and the diversity of population.And it takes the expectation and mean square error into account,which strengthens the ability of exploration.Meanwhile,a double-archive management strategy is carried out and updated in each iteration.One is used to store the non-dominated individuals and the other is used to build the surrogate process.Compared with the ParEGO,KRVEA,MOEAD and NSGAⅢ,R2-EMO algorithm has achieved a better performance and can converge to the Pareto front rapidly in dealing with expensive multi-objective optimization problems.
作者
刘建昌
赵阳杰
李飞
宋悦熙
LIU Jian-chang;ZHAO Yang-jie;LI Fei;SONG Yue-xi(College of Information Science and Engineering,Northeastern University,Shenyang 110004,China;School of Electrical and Information Engineering,Anhui University Technology,Maanshan 243032,China)
出处
《控制与决策》
EI
CSCD
北大核心
2020年第4期823-832,共10页
Control and Decision
基金
国家自然科学基金项目(61773106)
安徽省高校自然科学研究项目(KJ2019A0051).
关键词
昂贵多目标进化算法
R2指标
高斯过程
双层档案管理策略
expensive multi-objective evolutionary algorithm
R2 indicator
Gaussian process
double-archive management strategy
