两阶段模型协同搜索的昂贵多目标进化优化  

Expensive multi-objective evolutionary optimization with cooperative search of two-stage surrogate models

作　　者：刘晓彤 孙超利[2] 王浩[1] 谢刚 LIU Xiao-tong;SUN Chao-li;WANG Hao;XIE Gang(School of Electronic Information Engineering,Taiyuan University of Science and Technology,Taiyuan Shanxi 030024,China;College of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan Shanxi 030024,China)

机构地区：[1]太原科技大学电子信息工程学院,山西太原030024 [2]太原科技大学计算机科学与技术学院,山西太原030024

出　　处：《控制理论与应用》2024年第9期1676-1684,共9页Control Theory & Applications

基　　金：国家自然科学基金面上项目(61876123);山西省重点研发计划项目(202102020101002)资助.

摘　　要：近年来,昂贵多目标优化问题的求解获得了越来越多的关注.然而,随着决策空间维度的升高,模型的有效性和准确性很难保证.因此,本文提出了一种两阶段模型协同搜索的昂贵多目标进化优化.在该方法中,每轮种群进化前构建全局模型,以辅助加快对最优解集的搜索.随后,利用搜索到的种群选择其邻域样本训练局部模型,对二者集成辅助算法进行进一步搜索.最后,提出基于不确定度的填充采样策略选点,进行真实评价.为了验证算法的有效性,将本文算法与4个算法分别在DTLZ和MaF测试集以及两个实际问题上进行比较,实验结果表明其具有良好的性能.It has been paid more and more attention in recent years to solve expensive multi-objective optimization problems.However,it is challenging to train accurate and efficient models when the dimension of the decision space increases.Thus,expensive multi-objective evolutionary optimization with cooperative search of two-stage surrogate models(EMO-CS)is proposed in this paper for solving expensive problems.In the proposed method,a global model will be trained,before each iteration starts,to assist in speeding up the search for optimal solutions.Then a set of samples in the archive will be found and used to train a local model.The global and local models are used as an ensemble model,whose optimal solutions will be searched for and used to be selected for expensive objective evaluation based on the proposed uncertaintybased sampling criterion.Experimental results show that the proposed method performs better than four state-of-the-art algorithms on DTLZ and MaF test suites and two real-world optimization problems.

关 键 词：多目标优化 昂贵优化问题 集成模型 协同搜索 填充采样策略 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]