报告人：北京理工大学  田玉斌  教授

报告时间：2023年4月7日15：00-17：30

报告地点：河南省大数据研究院三楼大会议室

联系人：李文哲(电话号码:17796735786)

Abstract：

In optimization problems, the expensive black-box function implies severely restricted budgets in terms of evaluation. Some Bayesian optimization methods have been proposed to solve this problem, such as expected improvement(EI) and hierarchical expected improvement(HEI). However, most of these methods are nonparallel and use a one-point-at-a-time strategy. This study proposes a new parallel frame work that uses the minimum energy criterion. This frame work can reduce time cost by reducing the number of iterations and avoiding the local optimization trap by encouraging the exploration of the optimization space. We also propose a shrink-augment strategy to correct the local surrogate model for the black box function by placing more points around the true optima, which could also benefit the optimization. Some numerical experiments are also presented to compare the new method with popular existing methods. The results show the superiority of our proposed method over other Bayesian methods due to delivering better results with fewer iterations.

简介：

中国现场统计研究会试验设计分会副理事长，中国数学会均匀设计分会副主任委员。长期从事试验设计与可靠性优化方向的研究。先后主持国家自然科学基金委，科工局航天预先研究项目等。研究成果已经应用于我国十余种燃爆产品的可靠性分析、以及天宫系列卫星智能配电系统的设计与研发中。获得国防科技进步奖1项、全军科技进步奖1项。