Design of quantum computing-based avain navigation optimization algorithm for parameter estimation of input nonlinear output error model with key term separation

In recent years, quantum computing has been applied in optimizing different challenging systems in the domains of science and engineering. This work reflects the identification of the input nonlinear output error (I-NOE) model by using the Quantum-based avian navigation optimizer algorithm (QANA). QANA is an evolutionary method inspired by the precision navigation of migratory birds. The assessment of QANA is achieved on various iterations, population, and noise levels. The complexity, statistical, and convergence investigation of QANA with arithmetic optimization algorithm (AOA), coati optimization algorithm (COA), grey wolf optimizer (GWO), particle swarm optimization (PSO), synergistic swarm optimization algorithm (SSOA), and velocity pausing particle swarm optimization (VPPSO) approves the robustness of QANA for I-NOE model identification.