Enriched Imperialist Competitive Algorithm for system identification of magneto-rheological dampers

• Enriched Imperialist Competitive Algorithm is introduced to improve the results of non-linear system identification problems.
• The Bouc–Wen model׳s parameters for a MR damper are optimized using EICA algorithm.
• The obtained results demonstrate the high adaptability of EICA to suitably get to the bottom of such non-linear and hysteretic problems.

In the current research, the imperialist competitive algorithm is dramatically enhanced and a new optimization method dubbed as Enriched Imperialist Competitive Algorithm (EICA) is effectively introduced to deal with high non-linear optimization problems. To conduct a close examination of its functionality and efficacy, the proposed metaheuristic optimization approach is actively employed to sort out the parameter identification of two different types of hysteretic Bouc–Wen models which are simulating the non-linear behavior of MR dampers. Two types of experimental data are used for the optimization problems to minutely examine the robustness of the proposed EICA. The obtained results self-evidently demonstrate the high adaptability of EICA to suitably get to the bottom of such non-linear and hysteretic problems.