The employment of optimization algorithms and artificial intelligence constitutes a noticeable number of studies related to PEMFCs . To introduce the optimum operating conditions of an enhanced parallel flow field PEMFC, Ghasabehi et al. studied the impact of operating conditions on performance. They employed Response Surface Methodology (RSM) to present two equations describing output voltage and amount of maximum power density. Then, they revealed the ideal state with a suitable efficiency, power density, and lifetime. The ideal conditions are 3.35 atm, 328 K, saturated inlets, cathode stoichiometry 4.87, and anode stoichiometry 4.23. Likewise, Li et al. optimized the operating conditions and components size of a single straight channel PEMFC with three objectives named efficiency, oxygen distribution uniformity, and power density. Their reported ideal factors are 2.51 atm, 350.7 K, saturated inlets, GDL thickness 0.41 mm, membrane width 0.081mm, channel width 1.6 mm, and anode stoichiometry 1.5. Chen et al. investigated the role of operating conditions on the voltage consistency of a PEMFC stack and compared different machine learning methods to forecast the stack voltage consistency. An integrated regression method was the ideal method. Li and Eghbalian provided a modified flow field. They showed the application of the Black Widow Optimization Algorithm to catch the constants of an equation describing the cell's output voltage. There are different algorithms used to predict the output voltage of PEMFCs, such as Adaptive Sparrow Search Algorithm , Salp Swarm Algorithm , Sine-Cosine Crow Search Algorithm , Particle Swarm Optimization , Whale Optimization Algorithm , Hybrid Adaptive Differential Evolution Procedure , Grasshopper Optimization Algorithm , Particle Swarm Algorithm , Swarm Dolphin Algorithm , Grey Wolf Optimization Algorithm , Crow Search Algorithm , Teacher Learning Algorithm . The accuracy and runtime ratio of different optimization methods can be different. Some investigated compared optimization methods in specific cases. The pros and cons of various optimization models are weighted differently in different situations. A further comparison of the multi-objective version of the model for PEMFC with a variety of features and objectives will be helpful.