Improved manta ray foraging optimization for parameters identification of magnetorheological dampers

Abstract

Magnetorheological (MR) dampers play a crucial role in various engineering systems, and how to identify the control parameters of MR damper models without any prior knowledge has become a burning problem. In this study, to identify the control parameters of MR damper models more accurately, an improved manta ray foraging optimization (IMRFO) is proposed. The new algorithm designs a searching control factor according to a weak exploration ability of MRFO, which can effectively increase the global exploration of the algorithm. To prevent the premature convergence of the local optima, an adaptive weight coefficient based on the Levy flight is designed. Moreover, by introducing the Morlet wavelet mutation strategy to the algorithm, the mutation space is adaptively adjusted to enhance the ability of the algorithm to step out of stagnation and the convergence rate. The performance of the IMRFO is evaluated on two sets of benchmark functions and the results confirm the competitiveness of the proposed algorithm. Additionally, the IMRFO is applied in identifying the control parameters of MR dampers, the simulation results reveal the effectiveness and practicality of the IMRFO in the engineering applications.