基于带压缩因子粒子群算法标定设计反应谱

A response spectrum calibration method based on particle swarm optimization with compression factor

粒子群优化算法是模拟群体智能所建立起来的一种全局优化算法,在解决多参数非线性函数的优化问题上具有良好的性能,为了有更好的收敛精度和更快的收敛速度,本文构建了带有压缩因子的粒子群算法,可用于设计反应谱的标定。利用这一方法可给出第一拐点周期、特征周期、平台值和衰减指数等刻画设计反应谱特征的参数值。本文以埃尔森特罗台(El Centro)加速度时程的反应谱标定为例,采用本文提出的改进粒子群算法、Newmark三参数法、双参数法和差分进化算法对其进行标定。对比分析了4种标定方法给出的特征参数及计算精度,实例证明,改进粒子群算法具有较高的精度,给出的设计反应谱较好地反映了地震反应谱的特征。

Particle swarm optimization(PSO)is a global optimization algorithm based on the simulation of swarm intelligence.It has good performance in solving the optimization problem of multi-parameter nonlinear function.In order to have better convergence precision and faster convergence rate.In this paper,a particle swarm optimization algorithm with compression factor is proposed to calibrate the response spectrum.The characteristic parameters of seismic response spectrum,such as the first inflection period,characteristic period,platform value and attenuation index,Using this method can be given.The calibration of time-history response spectrum of El Centro acceleration is taken as an example.The improved particle swarm optimization(PSO)algorithm,Newmark three-parameter algorithm,two-parameter algorithm and differential evolutionary algorithm are used to calibrate it.The characteristic parameters and calculation accuracy given by the four calibration methods are compared and analyzed.The results show that the algorithm has high precision.The designed response spectrum reflects the characteristics of seismic response spectrum well.