摘要
由光束线状态决定的光束质量很大程度上影响着生物大分子晶体学的实验结果,目前上海光源晶体学线站的调光工作是由线站工作人员手动完成,费时费力。上海光源衍射线站采用差分进化算法实现了光束线样品点处光通量的单目标自动优化,但该方案仍然存在一定的局限性。为了进一步完善光束线自动优化方案和提高实验用户的束线机时使用效率,使用Python语言设计开发了基于带精英策略的非支配排序的遗传算法(Non Dominated Sorting Genetic Algorithm-Ⅱ,NSGA-Ⅱ)的多目标优化程序,实现了线站调光自动化技术升级。将该技术应用于上海光源BL10U2线站,以其次级光源点的光束位置和光通量为优化目标,可获得Pareto最优解集,并能从解集中根据实验需要自主选取合适的解。测试结果表明:相较于上海光源已有的自动优化方案,该多目标自动优化程序保证了光束线光束位置和光通量优化在30 min以内完成,进一步提高光束线自动优化效率和水平。
[Background]The beam quality determined by the beamline status greatly affects the experimental results of macromolecular X-ray crystallography(MX).At present,the optimization of the crystallography beamline of Shanghai Synchrotron Radiation Facility(SSRF)is manually operated by the beamline staffs,which is timeconsuming and laborious.X-ray diffractive(XRD)beamline of SSRF realized single-objective automatic optimization of flux based on differential evolution,but this scheme still has certain limitations for MX beamline.[Purpose]This study aims to design and implement an automatic optimization procedure based on non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)on the crystallography beamline system.[Methods]Firstly,based on NSGA-Ⅱ,a multi-objective automatic optimization model of the beamline was established.Then,an automatic optimization procedure was designed and implemented by using Python for the beamline system.Finally,this optimization procedure was tested on BL10U2 of SSRF to optimize the beam flux and position by adjusting the beamline optical components.[Results]The test results show that the automatic optimization can find the correct optimization objectives Pareto set with two optimized measures of beam flux and beam position within 30 min,and the optimization efficiency is greatly improved when compared with that of manual or previous optimization.[Conclusions]NSGA-Ⅱbased automatic optimization procedure simplifies the optical optimization of beamline and improves the operation efficiency of the crystallography beamlines at SSRF.
作者
张丁
吴盈锋
何迎花
刘科
汪启胜
何建华
ZHANG Ding;WU Yingfeng;HE Yinghua;LIU Ke;WANG Qisheng;HE Jianhua(Shanghai Institute of Applied Physics,Chinese Academy of Sciences,Shanghai 201800,China;University of Chinese Academy of Sciences,Beijing 100049,China;Shanghai Advanced Research Institute,Chinese Academy of Sciences,Shanghai 201210,China;The Institute for Advanced Studies,Wuhan University,Wuhan 430072,China)
出处
《核技术》
CAS
CSCD
北大核心
2021年第12期12-19,共8页
Nuclear Techniques
基金
国家重点研发计划(No.2017YFA0504901)资助。
