Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs  

作　　者：Andre K.Y.Low Flore Mekki-Berrada Abhishek Gupta Aleksandr Ostudin Jiaxun Xie Eleonore Vissol-Gaudin Yee-Fun Lim Qianxiao Li Yew Soon Ong Saif A.Khan Kedar Hippalgaonkar 

机构地区：[1]School of Materials Science and Engineering,Nanyang Technological University,Singapore 639798,Singapore [2]Institute of Materials Research and Engineering(IMRE),Agency for Science,Technology and Research(A*STAR),2 Fusionopolis Way,Innovis#08-03,Singapore 138634,Republic of Singapore [3]Department of Chemical and Biomolecular Engineering,National University of Singapore,Singapore 117585,Singapore [4]School of Mechanical Sciences,Indian Institute of Technology Goa,Goa 403401,India [5]Institute of Sustainability for Chemicals,Energy and Environment(ISCE2),Agency for Science,Technology and Research(A*STAR),1 Pesek Road,Singapore 627833,Republic of Singapore [6]Department of Mathematics,National University of Singapore,Singapore 119077,Singapore [7]Institute for Functional Intelligent Materials,National University of Singapore,Singapore 117544,Singapore [8]School of Computer Science and Engineering,Nanyang Technological University,Singapore 639798,Singapore

出　　处：《npj Computational Materials》2024年第1期2171-2181,共11页计算材料学(英文)

基　　金：funding from AME Programmatic Funds by the Agency for Science,Technology and Research under Grant No.A1898b0043 and No.A20G9b0135;KH also acknowledges funding from the National Research Foundation(NRF),Singapore under the NRF Fellowship(NRF-NRFF13-2021-0011);SAK and FMB also acknowledge funding from the 25th NRF CRP programme(NRF-CRP25-2020RS-0002);QL also acknowledges support from the NRF fellowship(project No.NRF-NRFF13-2021-0005);the Ministry of Education,Singapore,under its Research Centre of Excellence award to the Institute for Functional Intelligent Materials(I-FIM,project No.EDUNC-33-18-279-V12).

摘　　要：The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces.To reach target properties efficiently,these platforms are increasingly paired with intelligent experimental design.However,current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints.Here,we devise an Evolution-Guided Bayesian Optimization(EGBO)algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement(qNEHVI)optimizer;this not only solves for the Pareto Front(PF)efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space.

关 键 词：OPTIMIZATION driving CONSTRAINED 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]