In the past few decades, many optimization techniques have been employed in system identification problems. Most of these identification methods are calculus-based search methods. They are performed by point-to-point search strategy. A good initial guess of the parameter and gradient or higher-order derivatives of the objective function is normally required. There is a possibility to fall into a local minimum rather than the global minimum. On the contrary, genetic algorithms (GAs) are optimization procedures inspired by natural evolution. They model natural processes, such as selection, recombination, and mutation, and work on populations of individuals instead of single solutions. In this way, the algorithms are parallel and global search techniques that search multiple points; therefore, they are more likely to obtain a global solution. While the GA method has been developed as a powerful search tool in a global solution space, it is not necessarily efficient in fine-tuning for local convergence particularly when the search domain is large. In order to accelerate the convergence to the optimal solutions, a hybrid identification strategy, combining GA and local search technique, such as Gauss–Newton method is proposed in this paper. The proposed algorithm is explored by comparing the results of the predicted response with the measured response for both the single degree of freedom linear/nonlinear system and the multiple degree of freedom linear/nonlinear system with or without noise contamination. Finally, the hybrid computational strategy is also applied to the Taiwan Electricity Main Building using records from the 331 earthquake (2002). The comparison is made between the predicted acceleration and the measured acceleration for each case. Copyright © 2008 John Wiley & Sons, Ltd.
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Jan 1, 2023
Yuhan Feng 
Open Access