Research Information System
in: Handbook of Whale Optimization Algorithm, Mirjalili,Seyedali, Editor, Academic Press , London, pp.423-434, 2024
This chapter deals with one of the complex optimization problems known as system identification. The concept of the system identification has recently gained a considerable attention from the researchers in the field of science and engineering as it helps modeling the physical plants. The infinite impulse response (IIR) models help achieving more accurate models of the physical plants in real world; thus, they are favorable most compared to finite impulse response models. Metaheuristic optimizers can be used as efficient tools to benefit from the IIR models for the system identification. Therefore, this chapter aims to demonstrate the implementation procedure and the promise of the whale optimization algorithm as a new method to reach more accurate and robust IIR model identification. In that sense, several different metaheuristic algorithms are employed for comparative assessments against four different IIR models using the same-order and reduced order systems. Detailed statistical and convergence profile analyses show the competitive ability of the whale optimization algorithm for reaching robust and accurate solutions compared to sine-cosine, gravitational search and artificial bee colony optimization algorithms as other competitive metaheuristic based IIR model identification examples.