Identification of BoucWen type models using multi-objective optimization algorithms
Most of the published literature concerned with the parameter estimation of the BoucWen model of hysteresis via evolutionary algorithms uses a single objective function (the mean square error between the known displacements and the estimated ones) and considers the original BoucWen model of hysteresis (without degradation and pinching) in the identification process. In this paper, a novel method for the identification of the parameters of the BoucWenBaberNoori (BWBN) model of hysteresis is presented. The methodology is based on a multi-objective evolutionary optimization algorithm called NSGA-II [39]; therefore, a set of objective functions is employed instead of the traditional single objective function. The proposed methodology identifies the structural system and allows the observation of multi-modality of the BWBN model of hysteresis. The performance of the algorithm is evaluated using simulated and real data.
multi-objective optimization hysteresis nsga-ii boucwenbabernoori model system identification