Optimization of modified Bouc-Wen model for magnetorheological damper using modified cuckoo search algorithm

This article presents a new modified cuckoo search algorithm with dynamic discovery probability and step-size factor for optimizing the Bouc-Wen Model in magnetorheological damper application. The newly proposed algorithm was tested using a set of standard benchmark functions with different searching space and global optima placement. An engineering optimization application was chosen to evaluate the performance of the algorithm in complex engineering applications. The optimization task involved hysteresis parameter identification of the root mean square error between the model and an actual magnetorheological damper. The magnetorheological damper response was chosen as the objective function. The final value of the fitness function and the iteration number it took to converge were used as the qualifying indicator to the proposed cuckoo search algorithm efficiency. A comparison was done against particle swarm optimization, genetic algorithm, and sine-cosine algorithm, where the modified cuckoo search algorithm showed the lowest root mean square error and fastest convergence rate among the three algorithms.