控制理论与应用2016，Vol.33Issue(10)：1389-1398,10.DOI:10.7641/CTA.2016.50398
求解昂贵区间多目标优化问题的高斯代理模型
Gaussian surrogate models for expensive interval multi-objective optimization problem
摘要
Abstract
In this paper data mining (Gaussian process regression modeling) and intelligent evolutionary algorithm (GA, NSGA–II) are combined to solve the expensive interval multi-objective optimization problem with unknown optimization functions. Firstly, Gaussian process (GP) is used to model the objective functions and constraint functions represented by the midpoint and uncertainty. Because relevance and accuracy are two essential factors of interval function models, A kind of double steps screening strategy based on multiple attribute decision making (MADM) is proposed and it is embedded into the genetic algorithm to identify the parameters of the GP model. In the first step, inferior solutions in candidate solutions are excluded according to relevance. In the second step, the rest of inferior solutions beyond population quantity are excluded according to accuracy. And the proportion of inferior solutions excluded in the two steps is decided by the weight coefficient of two factors. Then, the built GP models for optimization objects are used as surrogate models in the NSGA-II optimization algorithm, so that Pareto front can be found.关键词
多目标优化/区间规划/第2代非支配排序进化算法(NSGA-II)/高斯过程/多属性决策/代理模型Key words
multi-objective optimization/interval programming/non-dominated sorting genetical agorithm II (NSGA-II)/Gaussian process/multiple attribute decision making/surrogate model分类
信息技术与安全科学引用本文复制引用
陈志旺,白锌,杨七,黄兴旺,李国强..求解昂贵区间多目标优化问题的高斯代理模型[J].控制理论与应用,2016,33(10):1389-1398,10.基金项目
国家自然科学基金(61403331,61573305),河北省自然科学基金青年基金(F2014203099),燕山大学青年教师自主研究计划课题(13LGA006)资助. Supported by National Natural Science Foundation of China (61403331,61573305), Natural Science Foundation for Young Scientist of Hebei Pro-vince, China (F2014203099) and Independent Research Program for Young Teachers of Yanshan University, China (13LGA006) （61403331,61573305）
