%0 Journal Article
%T GA-PSO ALGORITHM MODEL UPDATING<br>遗传-粒子群算法模型修正
%A 秦玉灵
%A 孔宪仁
%A 罗文波
%J 力学与实践
%D 2009
%I 
%X Integrant modal data are used to update a five-layer steel frame. The comparisons between the efficiencies and precisions of the Genetic Algorithm (GA), Particle Swarm Optimization Algorithm (PSO) and GA-PSO in the model updating processes show that all the four modal frequencies and modal shapes of the updated model can approach the target values with in varying degrees, which proves that these methods can all efficiently update the model. In particular, GA-PSO algorithm uses GA to efficiently search for the global-optical solution at an early stage, and uses PSO to intensively search for the local-optimal solution at a later stage. Comparing with the PSO and GA, GA-PSO algorithm enjoys higher updating efficiency and precision.
%K GA - PSO algorithm
%K model updating
%K global search
%K local search
%K updating efficiency<br>遗传-粒子群算法
%K 模型修正
%K 全局搜索
%K 局部搜索
%K 修正效率
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=5D344E2AD54D14F8&jid=0BF5C9FB031A532ED0A6D99DC4F6181A&aid=E5AA0CA06F42EE1CA626DCE3BA05310D&yid=DE12191FBD62783C&vid=4AD960B5AD2D111A&iid=94C357A881DFC066&sid=014B591DF029732F&eid=BFE7933E5EEA150D&journal_id=1000-0879&journal_name=力学与实践&referenced_num=3&reference_num=4