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In this study, an enhanced grey wolf optimizer with fusion strategies (EGWOFS) is proposed to overcome these shortcomings. First, a modified multiple learning backtracking search algorithm (MMLBSA) is designed to ameliorate the global exploration potential of the original GWO. Second, a dynamic spiral updating position strategy (DSUPS) is constructed to promote the performance of local exploitation. Finally, the proposed EGWOFS is verified by two groups of test data, which include three types of PV test models and experimental data extracted from the manufacturer\u2019s data sheet. Experiments show that the overall performance of the proposed EGWOFS achieves competitive or better results in terms of accuracy and reliability for most test models.<\/jats:p>","DOI":"10.3233\/ica-220693","type":"journal-article","created":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T11:24:35Z","timestamp":1666697075000},"page":"89-104","source":"Crossref","is-referenced-by-count":34,"title":["An enhanced grey wolf optimizer with fusion strategies for identifying the parameters of photovoltaic 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