When identifying the properties of existing structures, the so-called non-classical methods based on soft computing techniques have recently shown a promising robustness and efficacy. In particular, in the last decade an increasing attention has been paid on biologically-inspired routines (i. e., neural networks and genetic algorithms) to identify models characterized by linear as well as nonlinear behaviour. In this paper, an advanced genetic algorithm has been presented for parameter identification of single-degree-of-freedom nonlinear system when subject to ground acceleration, e. g. due to earthquakes. Specifically, the well known smooth endochronic Bouc-Wen model has been investigated. The proposed algorithm utilizes several subpopulations, and chromosomes are represented by means of real encoding. Moreover, recent developments in traditional genetic operators (crossover and mutation) are taken into account, so that the final algorithm combines an adaptive rebirth operator, a migration strategy and a search space reduction technique. The computational effectiveness and the accuracy of the proposed strategy have shown that it outperforms two existing conventional genetic algorithms: the Standard Genetic Algorithm and the Micro Genetic Algorithm. Secondarily, a comparative study is performed to demonstrate that an improved performance can be obtained by using, instead of structural acceleration response, its Hilbert transform-based envelope as objective function in the optimization problem. Finally, system identification is conducted by using the proposed optimizer to verify its substantial noise-insensitive property also in presence of high noise-to-signal ratio.
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