Hybrid of imperialist competitive algorithm and particle swarm optimization for parameter extraction of photovoltaic cells

To improve the efficiency of photovoltaic systems, it is essential to obtain the parameters of photovoltaic cells through an identification process. However, due to the nonlinear and multimodal characteristics, accurately and reliably identifying the parameters of photovoltaic cells still remains a challenging task. In this paper, a hybrid of the imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), ICA-PSO, is proposed to effectively identify the parameters of photovoltaic cells. The position updating strategy of PSO is adopted to replace the colony's position updating strategy in the ICA. The hybrid algorithm ICA-PSO integrates the multi-swarm search characteristic and the powerful exploration ability of PSO together, leading to an enhanced optimization performance. Experimental results of applying ICA-PSO to parameter identification of photovoltaic cells show that ICA-PSO can extract the parameters of photovoltaic cells with higher accuracy and reliability, thus outperforming many other methods presented in the literature.