Nonlinear hysteretic parameter identification using improved artificial bee colony algorithm

Abstract

Hysteresis is a common phenomenon arising in many engineering applications. It describes a memory-based relation between the restoring force and the displacement. Identification of the hysteretic parameters is central to practical application of the hysteretic models. To proceed so, a noteworthy thing is that the hysteretic models are often complex and non-differentiable so that getting the gradients is never straightforward and therefore, the swarm-based algorithm is often preferable to inverse hysteretic parameter identification. Along these lines, an improved artificial bee colony algorithm is developed in this paper for general hysteretic parameter identification. On the one hand, several hysteretic models along with the extensions to tackle the degradation and pinching behaviours are considered and how to model a structure with hysteretic components is also elaborated. As a result, the governing equation for the direct problem is established. On the other hand, the differential evolution mechanism is introduced to improve the original artificial bee colony algorithm. Numerical examples are conducted to testify the feasibility and accuracy of the proposed method in nonlinear hysteretic parameter identification.

Performance Evaluation Model of Corporate Financial Sustainability based on Swarm Algorithm

“…X Wang et al, to address the matter that it is difficult to efficiently and accurately perform single inversion for stratigraphic parameters Wang et al proposed an inversion method combining ABC and damped least squares algorithm to solve the problem of inversion and reconstruction of stratigraphic parameters in a single inversion, and the results showed that the method has not only the advantage of ABC, but also the high accuracy and fast convergence of damped least squares algorithm, this method is prone to getting stuck in local optima when using ABC to optimize the parameters of the least squares method [10]. R Yao et al found that the hysteresis model is complex and non-differentiable, which leads to the complexity of gradient acquisition, and to solve this problem, the authors proposed an improved ABC and used it for the identification of lag parameters, and finally verified the feasibility of the method in the identification of nonlinear hysteresis parameters, although this method improves the identification performance of nonlinear hysteresis parameters, it does not consider the identification problem of other linear parameters [11]. A Tuncer found that the traditional algorithm, can only provide small-scale solutions for the N-puzzle problem, to achieve the solution of the 15-puzzle problem, the authors proposed a new algorithm built on the metaheuristic algorithm, which divides the puzzle, and uses ABC to solve the divided the results show that the method helps significantly in solving the 15-puzzle problem, this method only divides the problem, simplifies the solving steps, but has little effect on the solving calculation of the problem [12].…”