The aim of this study is to analyze the exergy efficiency of a high‐temperature proton exchange membrane fuel cell. To do this purpose, meta‐heuristic technique has been used. First, the model of a membrane fuel cell is simulated and the polarization diagram shows a potent agreement with empirical data. Then, a new improved version of collective animal behavior algorithm is utilized for evaluating and optimizing the thermodynamic irreversibility, exergy efficiency, and work of the fuel cell. The algorithm uses opposition‐based learning and Lévy flight for improving the algorithm's premature convergence shortcoming. The result of this study shows that by comparison with standard collective animal behavior algorithm, genetic algorithm, and empirical results, the proposed algorithm has better achievements for both terms of optimal value finding and convergence strength.

中文翻译：

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应用元启发式方法优化HT-PEMFC的火用

这项研究的目的是分析高温质子交换膜燃料电池的（火用）效率。为了达到这个目的，已经使用了元启发式技术。首先，模拟了膜燃料电池的模型，极化图显示了与经验数据的有效一致性。然后，集体动物行为算法的一种新的改进版本被用于评估和优化热力学不可逆性，火用效率和燃料电池的工作。该算法使用基于对立的学习和Lévy飞行来改善算法的过早收敛缺点。研究结果表明，与标准集体动物行为算法，遗传算法和经验结果相比，