引用本文: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.[点击复制]
CHEN Zhi-wang,BAI Xin,YANG Qi,HUANG Xing-wang,LI Guo-qiang.Gaussian surrogate models for expensive interval multi-objective optimization problem[J].Control Theory and Technology,2016,33(10):1389~1398.[点击复制]
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
摘要点击 3733  全文点击 2015  投稿时间：2015-05-13  修订日期：2016-08-10
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DOI编号  10.7641/CTA.2016.50398
  2016,33(10):1389-1398
中文关键词  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
英文关键词  multi-objective optimization  interval programming  non-dominated sorting genetical agorithm II (NSGA-- II)  Gaussian process  multiple attribute decision making  surrogate mode
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.
作者单位E-mail
陈志旺 燕山大学 czwaaron@ysu.edu.cn 
白锌* 燕山大学 565141916@qq.com 
杨七 燕山大学  
黄兴旺 燕山大学  
李国强 燕山大学  
中文摘要
      本文将数据挖掘(高斯过程回归建模)和智能进化算法(GA, NSGA–II)进行结合, 用于解决优化函数未知的 昂贵区间多目标优化问题. 首先利用高斯过程对采用中点和不确定度表示的未知目标函数和约束函数进行建模, 由 于相关性和准确性是区间函数模型的两个必备条件, 故提出一种融合多属性决策的双层种群筛选策略, 并将其嵌入 到遗传算法求解高斯模型参数的过程中, 第1层根据相关性属性排除候选解集中部分劣解, 第2层根据准确性属性排 除候选解集中其余超出种群规模的劣解, 两属性的权重系数决定两层排除劣解的比例. 然后将所建模型作为优化 对象的代理模型引导区间NSGA–II算法优化求解, 从而获得所需的Pareto前沿.
英文摘要
      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.