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The efficient identification of the unknown and changeable photovoltaic parameters is a matter of considerable interest to model photovoltaic systems. The accurate and efficient parameters are important in converting the entire photovoltaic system from solar to electricity. This paper, an ensemble multi strategy-driven shuffled frog leading algorithm (EMSFLA), is proposed to optimize photovoltaic modules' parameters and enhance solar energy conversion efficiency. In the EMSFLA, opposition-based learning can consider the opposite position in each frog memeplex to enhance the convergence velocity and keep the population diversity. The mutation and crossover operators abstracted from differential evolution with greedy strategy can better balance diversification and intensification during the optimization process. Then, the performance of the EMSFLA is preliminarily verified on representative benchmark functions …