An improved firefly algorithm for identifying parameters of nonlinear empirical models

The nonlinear model describes the vortex-induced resonance of long-span bridges under the action of natural wind. The identification accuracy of its parameters directly affects the understanding of vortex-induced vibration. Different algorithms have been used to solve this parameter identification problem, but their efficiency and accuracy are not satisfactory. In this work, a firefly algorithm based on local chaos search and brightness variant (FACLBV) was proposed. The characteristics of chaos made FACLBV search the widely local scope and improve the accuracy of the solution. FACLBV modified the fixed initial brightness, discarded the absorption coefficient of light intensity, linked the initial brightness of every firefly with the position of its solution space, and set the attraction of every firefly as a simple linear function, which reduced the complexity of the algorithm and improved its efficiency. In order to better verify the superiority of FACLBV, the simulation experiment included three parts: a comparison between FACLBV and other firefly algorithms, the verification of the parameters identified by FACLBV, and the nonparametric test between FACLBV and other intelligent algorithms. Simulation results showed that the performance of FACLBV is better than that of other algorithms.