量子粒子群优化算法在结构参数识别中的应用

Application of Quantum Particle Swarm Optimization Algorithm in Structural Parameter Estimation

系统识别问题可以转化成一高维多模优化问题。针对基本粒子群优化在分析此类问题时容易出现早熟收敛从而导致局部优化和较大误差的缺陷,提出将量子粒子群优化算法(QPSO)应用于结构参数识别。QPSO具有参数少、编程简单、易实现、收敛速度快、可以避免早熟收敛、能够迅速在全局找到最优解的特点。本文在测量数据不完备且含噪声污染,参数质量、刚度和阻尼等信息缺乏的情况下,通过数值模拟以及在真实结构参数识别中的应用,验证了QPSO的有效性。

System identification can be formulated as a multimodal optimization problem with high dimension. The original particle swarm optimization （PSO） usually suffers from premature con- vergence tending to get stuck to local optima and low solution precision while solving these complex multimodal problems. In order to solve this problem, a quantum particle swarm optimization （QPSO） method was utilized to estimate parameters of structural systems. The potentialities of QPSO are its simple structure, less design parameters, easy use, fast convergence,premature convergence discouraged and global optimal searching property. The effectiveness of the proposed method was evaluated through the numerical simulations and an application to a real building. The effectiveness of the proposed method is evaluated through the numerical analysis and an application to a real building under conditions including limited measurement data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness.