基于集成分类器代理模型的昂贵多目标进化算法OACSCDCSTPCD
Evolutionary Algorithm with Ensemble Classifier Surrogate Model for Expensive Multiobjective Optimization
对于许多现实世界中的多目标优化问题,它们目标函数的评估在计算上通常成本高昂,这些问题称为昂贵多目标优化问题(Expensive multiobjective optimization problems,EMOP).现有的解决昂贵多目标优化问题的一种可行方式是引入计算效率较高的代理模型来减少函数评估的次数.受集成学习启发,本文针对EMOP,提出了基于集成分类器代理模型的多目标进化算法(Multiobjective evolutionary algo…查看全部>>
For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive. Such problems are usually called expensive multiobjective optimization problems (EMOPs). One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function evaluations. Inspired from ensemble learning,this paper proposes a multiobjective evolutionary algori…查看全部>>
蓝天
南京航空航天大学计算机科学与技术学院,南京 211106,中国
信息技术与安全科学
多目标进化算法昂贵多目标优化集成分类器代理模型
multiobjective evolutionary algorithmexpensive multiobjective optimizationensemble classifiersurrogate model
《南京航空航天大学学报（英文版）》 2020 (z1)
76-87,12
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