This paper introduces a novel butterfly optimization algorithm, called the spiral search and dynamic crossover based butterfly optimization algorithm (SCBO), for parameter estimation in proton exchange membrane fuel cell (PEMFC) models. To enhance the global performance of the butterfly algorithm, a spin-search strategy is incorporated to expand its exploration range, while an adaptive factor is introduced to strike a balance between exploration and exploitation. Additionally, a dynamic crossover operation is integrated to enhance solution diversity, addressing the algorithm's tendency to converge to local optima. Extensive experimentation on benchmark functions in comparison with common optimization algorithms demonstrates that SCBO outperforms others in terms of convergence accuracy and speed. Finally, we employ SCBO for parameter identification in a PEMFC model, showcasing its superior results and its ability to capture the model's dynamics when compared to other algorithms.

中文翻译：

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基于改进蝶形优化算法的 PEMFC 最优参数估计


本文介绍了一种新的蝶形优化算法，称为基于螺旋搜索和动态交叉的蝶形优化算法 （SCBO），用于质子交换膜燃料电池 （PEMFC） 模型中的参数估计。为了提高蝶形算法的全局性能，该文采用自旋搜索策略来扩大其探索范围，同时引入自适应因子来平衡探索和开发。此外，还集成了动态交叉操作以增强解的多样性，解决了算法收敛到局部最优的趋势。与常见优化算法相比，对基准函数进行的广泛实验表明，SCBO 在收敛精度和速度方面优于其他函数。最后，我们使用 SCBO 在 PEMFC 模型中进行参数识别，展示了与其他算法相比，其卓越的结果和捕获模型动态的能力。