Meta-heuristic optimization algorithms aim to tackle real world problems through maximizing some specific criteria such as performance, profit, and quality or minimizing others such as cost, time, and error. Accordingly, this paper introduces an improved version of a well-known optimization algorithm namely Archimedes optimization algorithm (AOA). The enhanced version combines two efficient strategies namely Local escaping operator (LEO) and Orthogonal learning (OL) to introduce the (I-AOA) optimization algorithm. Moreover, the performance of the proposed I-AOA has been evaluated on the CEC’2020 test suite, and three engineering design problems. Furthermore, I-AOA is applied to determine the optimal parameters of polymer electrolyte membrane (PEM) fuel cell (FC). Two commercial types of PEM fuel cells: 250W PEMFC and BCS 500W are considered to prove the superiority of the proposed optimizer. During the optimization procedure, the seven unknown parameters (${\xi }_1$, ${\xi }_2$, ${\xi }_3$, ${\xi }_4$, $\lambda$, $RC$, and $b$) of PEM fuel cell are assigned to be the decision variables. Whereas the cost function that required to be in a minimum state is represented by the RMSE between the estimated cell voltage and the measured data. The obtained results by the I-AOA are compared to other well-known optimizers such as Whale Optimization Algorithm (WOA), Moth-Flame Optimization Algorithm (MFO), Sine Cosine Algorithm (SCA), Particle Swarm Optimization Algorithm (PSO), Harris hawks optimization (HHO), Tunicate Swarm Algorithm (TSA) and original AOA. The comparison confirmed the superiority of the suggested algorithm in identifying the optimum PEM fuel cell parameters considering various operating conditions compared to the other optimization algorithms.

元启发式优化算法旨在通过最大化某些特定标准（如性能、利润和质量）或最小化其他标准（如成本、时间和错误）来解决现实世界的问题。因此，本文介绍了著名优化算法的改进版本，即阿基米德优化算法 (AOA)。增强版结合了本地转义算子（LEO）和正交学习（OL）两种有效策略，引入了（I-AOA）优化算法。此外，提议的 I-AOA 的性能已经在 CEC'2020 测试套件和三个工程设计问题上进行了评估。此外，I-AOA 用于确定聚合物电解质膜 (PEM) 燃料电池 (FC) 的最佳参数。两种商业类型的 PEM 燃料电池：250W PEMFC 和 BCS 500W 被认为证明了所提出的优化器的优越性。在优化过程中，七个未知参数（${\xi }_{\mathrm{1个}}$, ${\xi }_2$, ${\xi }_3$, ${\xi }_4$, $\lambda$, $\mathrm{电阻}C$， 和 $乙$) 的 PEM 燃料电池被指定为决策变量。而需要处于最小状态的成本函数由估计的电池电压和测量数据之间的 RMSE 表示。将 I-AOA 获得的结果与其他著名的优化器进行了比较，例如 Whale 优化算法 (WOA)、Moth-Flame 优化算法 (MFO)、正弦余弦算法 (SCA)、粒子群优化算法 (PSO)、Harris鹰派优化 (HHO)、Tunicate Swarm 算法 (TSA) 和原始 AOA。与其他优化算法相比，该比较证实了所建议算法在考虑各种操作条件的情况下在确定最佳 PEM 燃料电池参数方面的优越性。