Parameter estimation using a CLPSO strategy

As a novel evolutionary computation technique, particle swarm optimization (PSO) has attracted much attention and wide applications for solving complex optimization problems in different fields mainly for various continuous optimization problems. However, it may easily get trapped in a local optimum when solving complex multimodal problems. This paper utilizes an improved PSO by incorporating a comprehensive learning strategy into original PSO to discourage premature convergence, namely CLPSO strategy to estimate parameters of structural systems, which could be formulated as a multi-modal optimization problem with high dimension. Simulation results for identifying the parameters of a structural system under conditions including limited output data and no prior knowledge of mass, damping, or stiffness are presented to demonstrate the effectiveness of the proposed method.