Effectiveness of the Bio-Inspired Firefly Algorithm in Adaptive Signal Processing for Nonlinear Systems

In this paper the performance of the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) algorithm, the Modified Particle Swarm Optimization (MPSO) algorithm, and the Lévy Flight Firefly Algorithm (LFFA) are compared for system identification with various types of nonlinear systems. When performing system identification with Volterra nonlinear adaptive structures, matched-order fixed-nonlinearity LNL filter structures, reduced-order adaptable-nonlinearity LNL filter structures and neural networks the LFFA generally demonstrates faster convergence rates and lower minimum mean square errors (MMSE) compared to the GA, PSO, and MPSO algorithms. This work includes performance comparisons of these algorithms when applied to nonlinear system identification.