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About 3500 samples were applied for the training process, and almost 900 samples were utilized for the testing. After modeling process, the genetic algorithm, particle swarm, simulated annealing, and hybrid firefly-particle swarm optimizers are applied to achieve the optimum value of current and power densities. The results showed that proposed fuzzy model could approximate the model the system with a good agreement with experimental data. Additionally, the obtained data confirm the accuracy, high convergence speed, and robustness of the proposed hybrid optimizer compared to three efficient optimization algorithms. Accordingly, the correlation factor for the proposed fuzzy model for the training and testing dataset was obtained to be 0.9298 and 0.9289, correspondingly.<\/jats:p>","DOI":"10.3233\/jifs-221125","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T10:58:59Z","timestamp":1684234739000},"page":"845-862","source":"Crossref","is-referenced-by-count":1,"title":["Performance improvement of the solid oxide fuel cell using optimal parameters identification through fuzzy logic based-modeling and different optimization algorithms"],"prefix":"10.1177","volume":"45","author":[{"given":"Guomin","family":"Chen","sequence":"first","affiliation":[{"name":"School of Management, Guilin University of Aerospace Technology, Guilin, China"}]},{"given":"Yingwei","family":"Jin","sequence":"additional","affiliation":[{"name":"Tongji University Press, Shanghai, China"}]},{"given":"Shili","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Management, 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