The Application Of Improved Quantum-behaved Particle Swarm Optimization In Structural Identification

Due to the combined effect of material aging, external environment, overload operation and so on, structural accidents occurred frequently. Therefore, Structural health monitoring(SHM) emerged. Structural modal parameters identification as the core of SHM has lots of methods. But due to various reasons, it cannot be utilized effectively in the actual project. With the rapid development of information technology and computer technology, intelligent optimization technology is gradually being introduced into the structural parameters identification. Among them, quantum particle swarm optimization(QPSO) algorithm has become widely accepted because of its numerous advantages. However, QPSO also has deficiencies like the global optimization ability is not strong and the algorithm is easy to fall into local optimum. For the lacks of QPSO, Mixed-probability based new wavelet mutation quantum-behaved particle swarm optimization(M-WMQPSO) algorithm and quantum-behaved particle swarm optimization based assimilate and competition(ACQPSO) algorithm are proposed separately in this paper. And then apply them into structural parameters identification. Specific content and results are as follows:1?The introduction of structural parameters identification, the applications of intelligent optimization algorithm in structural parameter identification and the theory of QPSO.2?Analysis of the deficiencies of QPSO, and then put forward the need of improvement.3?Develop the M-WMQPSO algorithm. And introduce the principle of algorithm and the process of operation. Then verify the algorithm by standard test functions.4?Develop the ACQPSO algorithm. And introduce the thought of algorithm and the process of operation. The results of standard test functions show that ACQPSO improves the global optimization ability of QPSO greatly.5?Apply these two improved algorithm into structural parameters identification. The numerical simulations of three mass model, six story frame structure model and simply supported beam model and a test of three story frame structure are carried out under ambient excitation. The results indicate that the improved algorithm is superior to QPSO greatly in the accuracy and noise immunity.