基于同化竞争QPSO算法的结构模态参数识别  

Structural modal parameters identification based on assimilation competitive QPSO algorithm

作　　者：孟浩 卢俊龙[2] MENG Hao;LU Junlong(Jiangsu Grand Canyon Architectural Design Co.Ltd.Suzhou 215000,China;School of Civil engineering and Architecture,Xi'an University of Technology,Xi'an 710048,China)

机构地区：[1]江苏合谷建筑设计有限公司,江苏苏州215000 [2]西安理工大学土木建筑工程学院,陕西西安710048

出　　处：《苏州科技大学学报（工程技术版）》2020年第1期50-56,共7页Journal of Suzhou University of Science and Technology（Engineering and Technology Edition）

基　　金：国家自然科学基金项目(51778527)。

摘　　要：针对粒子群优化算法整体寻优能力不足的问题,增强粒子寻优速度,并降低陷入局部最优的风险。论文基于同化竞争思想,提出了一种改进的量子粒子群优化算法。采用同化竞争手段对粒子寻优过程进行改良,具体过程为:采用全局最优粒子作为中心,对其余粒子进行同化影响;同时,保证各个粒子之间保持竞争关系,以促进粒子寻优速度的提升。本文针对简支梁模型的动力模态,采用该算法进行模拟识别,结果显示,该算法与量子粒子群算法相比,分析精度及抗干扰能力都得到明显提高。最后,针对三层框架结构进行振动试验,并采用该方法进行分析,结果显示,两者吻合度较好,验证了该方法的有效性。As to the shortcomings of the particle swarm optimization algorithm,the speed of particle optimization can be enhanced,and the risk of falling into a local optimum should be reduced.This paper proposes an improved quantum particle swarm optimization algorithm based on the assimilation competition method,which is used to improve the particle optimization process.The specific process is to use the global optimal particle as the center to assimilate the remaining particles;at the same time,a competitive relationship between each particle should be maintained in order to promote the improvement of the particle optimization speed.In this paper the algorithm for the simulation identification is used to deal with the dynamic mode of the simply supported beam model.The results show that the algorithm has significantly improved the analysis accuracy and anti-interference ability compared with the quantum particle swarm algorithm.Finally,a three-story frame structure was subjected to vibration tests and analyzed using this method.The results show that the two agree well with each other,verifying the effectiveness of the method.

关 键 词：量子粒子群 优化算法 同化竞争 参数识别 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]