中山大学学报（自然科学版）（中英文）2017，Vol.56Issue(6)：105-110,6.DOI:10.13471/j.cnki.acta.snus.2017.06.016
基于改进大爆炸算法的结构损伤识别
Structure damage detection based on improved big bang-bigcrunch algorithm
摘要
Abstract
The Big Bang-Big Crunch (BB-BC) algorithm is an optimization technique of swarm intelligence based on the Big Bang theory.It runs efficiently and can be realized simply,but it is easily trapped in local optimal results.For the purpose of overcoming its shortages,an improved BB-BC algorithm is put forward in this essay,with the changes of the reduced forms of blast radius,the distribution of the random variable,and the treatment of the boundary conditions.Besides,the improved algorithm is applied in damage detection of a simply supported beam with 10 and 20 elements respectively.The numerical simulations indicate that the identified results are excellent even in the great influence of noise,especially for successive elements with tiny damage.A conclusion can be drawn that the improved BB-BC algorithm can precisely detect structure damage,and would not be easily trapped into local optimal.关键词
群智能/大爆炸算法/结构损伤识别/频域Key words
swarm intelligence/BB-BC/damage detection/frequency domain分类
通用工业技术引用本文复制引用
尹智毅,张艾迪,林楷钊,吕中荣..基于改进大爆炸算法的结构损伤识别[J].中山大学学报（自然科学版）（中英文）,2017,56(6):105-110,6.基金项目
国家自然科学基金(11172333,11272361) （11172333,11272361）
广东省科技计划项目(2016A020223006) （2016A020223006）
广东省自然科学基金(2015A030313126) （2015A030313126）
