A novel metaheuristic optimizer based on improved adaptive guided differential evolution algorithm for parameter identification of a PEMFC model

•An improved AGDE algorithm is proposed for PEMFC parameter identification.•A RFDB method is proposed by adding the RWS strategy.•The combination of RFDB and LF is used to optimize the mutation mechanism of AGDE.•Comparison with a full range of seven intelligent algorithms. Proton exchange membrane fuel cells (PEMFCs) have the benefits of high efficiency, fast startup, and the ability to operate at low temperatures, which can improve the efficiency of energy utilization. Accurate parameter identification can enable the PEMFC model to better predict and simulate the performance of the system under dynamic operating conditions. Based on the semi-empirical model of PEMFC, an improved adaptive guided differential evolution (AGDE) algorithm is presented by adding the roulette wheel selection (RWS) optimization-based fitness-distance balance (RFDB) and Levy flight (LF) strategies, simplified as LRFDB-AGDE. The integration of multiple enhancement strategies is for a deeper optimization of the mutation mechanism architecture of the AGDE algorithm, aiming to enhance the local and global integrated search ability of the LRFDB-AGDE algorithm, which can identify the unknown parameters of the PEMFC model more efficiently and quickly. In this study, the superior parameter identification performance of the proposed LRFDB-AGDE algorithm is validated by simulating voltage and current data from four types of PEMFCs and comparing them with traditional intelligent algorithms such as AGDE and the whale optimization algorithm (WOA). Notably, the absolute errors of the LRFDB-AGDE algorithm in identifying the four PEMFCs are all within 5%.