Identification of Bouc-Wen type models using multi-objective optimization algorithms

Most of the published literature concerned with the parameter estimation of the Bouc-Wen 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 Bouc-Wen 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 Bouc-Wen-Baber-Noori (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. (C) 2012 Elsevier Ltd. All rights reserved.