An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification

Abstract

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 (ξ1, ξ2, ξ3, ξ4, λ, 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.