Nonlinear system identification using a semi concurrent sequential niching framework

System identification is a very critical process in a control system aiming to determine the system dynamics of practical processes before devising an adequate control approach. Several metaheuristic approaches have been used for the parametric estimation of nonlinear systems, ranging from classical to new-generation metaheuristics. Nevertheless, the stochastic nature of these algorithms does not guarantee the obtention of the true optima solution thus leaving room for more explorative algorithmic enhancements. The current research proposes the use of a computationally efficient semi-concurrent sequential niching framework to boost the search performance of the parameter estimation task in a bid to obtain more accurate parameters. This framework has been validated in our previous studies on multimodal test functions and is here applied for system identification of nonlinear models, particularly PV model estimation.