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Author Spotlight: Design and Evaluation of Au-Electroplated Carbon Fiber Cloth Electrodes for Hydrogen Peroxide Fuel Cells
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HGOA-based framework for multi-objective optimization and performance prediction of PEM fuel cells.

Kamal1, Manish Kumar Singla2,3, Ramesh Kumar1,4

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.

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|October 14, 2025
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Summary

A new Hybrid Grasshopper Optimization Algorithm (HGOA) accurately models Proton Exchange Membrane Fuel Cells (PEMFCs) and optimizes multi-objectives. This advanced algorithm offers improved efficiency and reliability for clean energy applications.

Keywords:
Hybrid grasshopper optimization algorithm (HGOA)Metaheuristic algorithmsMulti-objective optimizationPerformance predictionProton exchange membrane fuel cell (PEMFC)

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Area of Science:

  • Energy Systems Engineering
  • Computational Intelligence
  • Electrochemical Systems

Background:

  • Proton Exchange Membrane Fuel Cells (PEMFCs) are crucial for clean energy due to high efficiency and zero emissions.
  • PEMFCs exhibit complex nonlinear dynamics and sensitivity to operating conditions, posing challenges for parameter identification and performance prediction.
  • Existing metaheuristic algorithms often suffer from premature convergence, computational inefficiency, and instability in complex systems.

Purpose of the Study:

  • To introduce a novel Hybrid Grasshopper Optimization Algorithm (HGOA) for accurate PEMFC modeling and multi-objective optimization.
  • To address the limitations of conventional metaheuristic approaches in handling complex PEMFC systems.
  • To enhance the accuracy, efficiency, and robustness of PEMFC parameter tuning and performance prediction.

Main Methods:

  • Development of the Hybrid Grasshopper Optimization Algorithm (HGOA) by integrating elite retention, opposition-based learning, feasibility repair, and local search into the standard Grasshopper Optimization Algorithm (GOA).
  • Application and validation of HGOA across seven diverse PEMFC test cases (FC1-FC7) under various operating conditions.
  • Comparative analysis of HGOA against nine state-of-the-art metaheuristic algorithms, including GOA, ECO, RIME, EO, and PO.

Main Results:

  • HGOA demonstrated superior accuracy in PEMFC modeling, achieving the lowest Absolute Error (AE = 0.0026) and Relative Error Percentage (RE% = 0.0613%).
  • Near-zero Mean Bias Error (MBE) was recorded across all tested PEMFC cases, indicating high predictive reliability.
  • HGOA exhibited enhanced computational efficiency, stability, and solution diversity compared to existing algorithms.

Conclusions:

  • The proposed HGOA is a highly accurate and efficient metaheuristic algorithm for modeling and optimizing PEMFCs.
  • HGOA overcomes the drawbacks of traditional methods, offering robust performance for complex electrochemical systems.
  • This advancement holds significant potential for improving the design, control, and application of PEMFCs in clean energy solutions.