Grey Wolf optimizer with ranking-based mutation operator for IIR model identification

Abstract

A variant of Grey Wolf optimizer (GWO), called grey Wolf optimizer with Ranking-based mutation operator (RGWO) is applied to the Infinite impulse response (IIR) system identification problem. RGWO makes GWO faster and more robust. In RGWO, the ranking-based mutation operator is integrated into the GWO to accelerate the convergence speed, and thus enhance the performance. The simulation results over several models are presented and statistically validated. Compared to other robust evolutionary algorithms, RGWO performs significantly better in terms of the quality, speed, and the stability of the final solutions.