Effects of the Particle Swarm Optimization parameters for structural dynamic monitoring of cantilever beam

Nowadays, particle swarm optimization (PSO) algorithm has become a widespread optimization method. However, it is well known that its main parameters (inertia weight, two learning factors, velocity constraint and population size) have a critical effect on its performance. Currently the effects of PSO parameters on structural health monitoring have not been comprehensively studied. Therefore, in this paper, the PSO algorithm is used for damage detection assessment of a cantilever beam, and the simulation results are used to analyze the effects of PSO parameters. There are five levels for each parameter in our experiment, mean fitness value and success rate for each level are used as criteria to measure the convergence and stability of the PSO algorithm. Considering the effect of population size on CPU time, a trade-off strategy is presented to further determine the selection of population size.