摘要
【目的】提出一种求解带有隐藏约束的昂贵黑箱优化问题的新响应面方法。【方法】对SHEBO算法进行了改进,取消了MADS强化搜索这一步骤,节约了昂贵黑箱目标函数的估值次数,并改善了响应面模型的更新策略,从而提高了算法效率。【结果】得到了新的带有隐藏约束昂贵黑箱优化问题的响应面方法。【结论】通过50个标准的测试问题对新算法的数值表现进行了评估,结果表明新算法优于原有的SHEBO方法。
[Purposes]To propose a new algorithm for solving expensive black-box optimization problems with hidden constraints.[Methods]Based on the framework of the SHEBO algorithm,the MADS intensified search step is dropped,which saves the number of expensive black-box objective function evaluations,and the update strategy of the response surface model is modified,to improve the efficiency of the algorithm.[Findings]A new response surface method for expensive black-box optimization problems with hidden constraints is obtained.[Conclusions]The numerical performance of the new algorithm is evaluated on 50 standard test problems,and the results show that the proposed algorithm is better than the original SHEBO algorithm.
作者
黄可晴
白富生
申富伟
HUANG Keqing;BAI Fusheng;SHEN Fuwei(School of Mathematical Sciences,Chongqing Normal University;National Center for Applied Mathematics in Chongqing,Chongqing 401331;Changsha County Vocational Secondary School,Changsha 410100,China)
出处
《重庆师范大学学报(自然科学版)》
CAS
北大核心
2022年第4期21-31,共11页
Journal of Chongqing Normal University:Natural Science
基金
国家自然科学基金(No.11991024)
重庆市自然科学基金(No.cstc2019jcyj-msxmX0386)。
关键词
隐藏约束
昂贵黑箱函数
响应面方法
径向基函数
hidden constraints
expensive black-box optimization
response surface method
radial basis function