A large domain identification problem in nonlinear systems using metaheuristics

Parameter identification in systems with nonlinear behavior in a large domain search space is one of the challenging engineering problems. The use of efficient optimization methods is one of the basic requirements for these problems. A nonlinear modified Bouc-Wen model of magneto-rheological dampers was selected as an engineering problem. Different cases of the large domain for search space are considered. In this paper, the imperialist competitive optimization, black hole algorithm, and grasshopper optimization algorithm are selected to solve the parameter identification of the Bouc-Wen model for the magneto-rheological damper. The results demonstrate that meta-heuristics have a high ability to tackle highly nonlinear problems. Also, they can preserve their performance for large domain optimization problems.