量子粒子群算法在结构模态参数识别中的应用

量子粒子群算法作为粒子群算法的改进,具有参数少、好编程、易收敛等优势而备受关注.通过将由结构输入、输出数据计算而得的实测频响函数与包含所需识别的结构模态参数的理论频响函数之差最小化作为优化目标,经过对理论频响函数中的结构模态参数搜索取值而使目标函数最小,此过程将结构模态参数识别问题转化为优化问题.采用量子粒子群算法进行优化而得到结构模态参数.为验证该方法的有效性,对一数值模拟的三层混凝土框架结构进行分析,结果表明,量子粒子群可以有效地识别结构模态参数.

Application of quantum-behaved particle swarm optimization for structural modal parameter identification

Quantum-behaved Particle Swarm Optimization, as a development of Particle Swarm Optimization, is an optimization algorithm based on the swarm intelligence. Thanks to its advantages of less parameter, simple programming, easy convergence and fast convergence, Quantum-behaved Particle Swarm Optimization received much concern. The minimization of difference between theoretical and test value of frequency response function, in which the former is a formula including modal parameters, and the later is calculated based on the input and output data of structure, will be adopted as an objection function of optimization issue. The optimal objective val- ue can be gained through searching reasonable modal parameters. Then, the issue of structural modal identifica- tion is converted to an optimization issue. During the optimization procedure, Quantum-behaved Particle Swarm Optimization is adopted and the modal parameters are identified. Finally, the modal parameter identification method based on Quantum-behaved Particle Swarm Optimization presented herein will be verified by a numerical simulation of three-story frame structure. The calculation results show that the method can effectively identify the structural modal parameters.