The main objective of this paper is to investigate efficiency and correctness of different real-
coded genetic algorithms and identification criteria in nonlinear system identification within …

A modified real‐coded genetic algorithm to identify the parameters of large structural
systems subject to the dynamic loads is presented in this article. The proposed algorithm …

This article introduces a robust hybrid computational method for the data‐driven model‐free
identification of nonlinear systems that exhibit hysteretic behavior. The proposed approach …

In this article, a new two-stage hybrid optimization method based on the Particle Swarm
Optimization and the Big Bang-Big Crunch algorithm (BB-BC) is introduced for identification …

In the past few decades, many optimization techniques have been employed in system
identification problems. Most of these identification methods are calculus‐based search …

Engineering applications of swarm intelligence optimization algorithms have been widely
employed to identify structural systems and damages owing to their merits of simplicity …

This work aims to provide a broad overview of computational techniques belonging to the
area of artificial intelligence tailored for identification of nonlinear dynamical systems. Both …

A new identification method that determines the parameters of Bouc–Wen hysteretic systems
is presented. It is based on a hybrid evolutionary algorithm which utilizes selected stochastic …

Several variants of differential evolution (DE), particle swarm optimization, and genetic
algorithms are employed for the identification of a Bouc-Wen hysteretic system that …

By identifying changes in stiffness parameters, structural damage can be detected and
monitored. Although considerable progress has been made in this research area, many …