改进QPSO算法识别结构模态参数

Structural Modal Parameter Identification Based on Improved QPSO Algorithm

为了进一步增强量子粒子群优化算法的全局寻优能力,提高粒子寻优效率,改善其容易陷入局部最优的缺陷,首先在引入同化和竞争思想的基础上提出一种改进的量子粒子群算法。该改进算法将民族间的同化竞争思想引入粒子寻优过程,以全局最优粒子作为中心粒子,不断同化其余粒子,使粒子之间保持不断竞争关系,以改进粒子的进化方式,提高粒子的寻优性能。接着将改进算法应用于结构模态参数识别,并采用简支梁数值模型对该算法的有效性进行验证,结果表明,改进算法较量子粒子群算法的识别精度和抗噪性都有显著的提高。最后通过三层框架试验验证改进算法在实际工程应用中的有效性。

To further enhance the ability of quantum-behaved particle swarm optimization(QPSO)algorithm in global optimization,raise the efficiency of particles searching and overcome the defect that QPSO may fall into the local optimum easily,an improved QPSO algorithm is proposed.The idea of national assimilation and competition is introduced into the particle optimization process.In the improved QPSO,the global best particle,treated as the central particle,assimilates the other particles constantly.At the same time,competition is maintained among the particles to improve the optimization performance of the algorithm.Then,the improved QPSO is applied to the structural modal parameters identification.The results of numerical simulation of a simply supported beam show that the identification accuracy and the noise-immunity are both improved remarkably.At last,the test of a3-story frame structure is carried out to verify the practicability and reliability of the improved QPSO.