Table 1 Provides a comparative analysis of key studies, emphasizing their contributions, limitations, and research gaps.
From: A novel Parrot Optimizer for robust and scalable PEMFC parameter optimization
References | Algorithm/Technique | Contribution | Limitations | Research Gaps |
|---|---|---|---|---|
Kouache et al.1 | Self-adaptive bonobo optimizer | Improved accuracy and computational efficiency for PEMFC parameter estimation | Computational overhead in scaling to larger systems | Scalability and real-time application |
El-Fergany et al.2 | Red-Billed Blue Magpie Optimizer | Enhanced electrical characterization of PEMFCs | Lack of comparative results with state-of-the-art methods | Robustness in large-scale systems |
Saidi et al.3 | Enhanced salp swarm algorithm | Robust parameter identification under various conditions | No real-world experimental validation | Experimental validation under dynamic conditions |
Elfar et al.4 | Particle swarm optimization | Effective PEMFC parameter identification | Limited validation under varying operating conditions | Comprehensive validation across diverse conditions |
Yang et al.5 | Neural network with pelican optimization | Reduced computational time with high accuracy | Scalability issues not deeply examined | Scalability to larger systems |
Shaheen et al.6 | Human memory optimizer | Robust PEMFC modeling with sensitivity analysis | Computational intensity in real-time applications | Real-time computational efficiency |
Sultan et al.7 | Modified manta ray foraging optimization | Enhanced precision in parameter identification | Limited application to diverse fuel cell types | Adaptability to different fuel cell types |
Houssein et al.8 | Walrus Optimizer | Significant accuracy improvements in parameter extraction | Lack of detailed comparative analysis with traditional methods | Comparative analysis with traditional methods |
Priya et al.9 | Clan co-operative spotted hyena optimizer | Computational efficiency in PEMFC modeling | Limited exploration of parameter variability under different conditions | Parameter variability under dynamic conditions |
Ashraf et al.10 | AI-based techniques for SOFCs | Comprehensive survey of optimization strategies | Focus on solid oxide fuel cells, limiting direct applicability to PEMFCs | Direct applicability to PEMFCs |
Zhang et al.11 | Swarm intelligence algorithm | High accuracy in PEMFC parameter identification | Limited insights into computational scalability for large datasets | Scalability to large datasets |
Ebrahimi et al.12 | Repairable Grey Wolf Optimization | Significant accuracy in parameter identification | No experimental validation of simulation results | Experimental validation |
Duan et al.13 | Amended deer hunting optimization | Robustness in PEMFC parameter estimation | Lack of detailed sensitivity analysis | Sensitivity analysis under varying conditions |
He et al.14 | Generalized regression neural networks with meta-heuristic algorithms | Effectiveness in PEMFC parameter identification | Computational challenges for real-time applications | Real-time computational efficiency |
Rubio et al.15 | Distributed intelligence for autonomous PEMFC control | Improved control system efficiency | Limited insights into scalability and adaptability | Scalability and adaptability |
Ali et al.16 | Coot bird optimizer | Adaptability to diverse datasets | Requires further validation in practical applications | Practical application validation |
Abdel-Basset et al.17 | Comparative study of recent methods | Superiority of specific approaches in PEMFC optimization | Lack of insights into implementation challenges | Implementation challenges |
Yang et al.18 | Review of mass transfer mechanisms in PEMFCs | Detailed theoretical foundation | No computational modeling validation | Computational modeling validation |
Guo et al.19 | Digital twin model for hybrid PV-SOFC systems | Potential implications for PEMFC modeling | Focus on SOFCs, limiting direct applicability to PEMFCs | Direct applicability to PEMFCs |
Mitra et al.20 | Comparative review of parameter estimation methods | Highlighted key methodological gaps | Limited addressing of implementation challenges in real-world scenarios | Real-world implementation challenges |
Liu et al.21 | Hybrid PSO with differential evolution | Innovation in PEMFC parameter identification | Computational intensity for large-scale applications | Scalability to large-scale systems |
Abdel-Basset et al.22 | Improved metaheuristic algorithms | Detailed comparative study of PEMFC parameter selection | Lack of experimental validation | Experimental validation |
Wang et al.23 | Improved chicken swarm optimization | Effective PEMFC model parameter estimation | Limited validation against experimental data | Extensive experimental validation |
Rezk et al.24 | Recent optimization algorithms | High accuracy in PEMFC parameter identification | Lack of comprehensive scalability analysis | Scalability analysis |
Zhou et al.25 | Improved fish migration optimization | Novel potential in PEMFC parameter identification | No addressing of long-term performance stability | Long-term performance stability |
Shalaby et al.26 | Membrane technologies | Indirect implications for PEMFCs | Focus on water treatment, limiting direct relevance | Direct relevance to PEMFCs |
Wilberforce et al.27 | Neural networks for PEMFC power and voltage prediction | High accuracy in prediction | Limited exploration of model adaptability to varied operating conditions | Adaptability to varied conditions |
Li et al.28 | Deep reinforcement learning for PEMFC control | Promising results in multi-system coordination | Computational demands as a limitation | Computational efficiency |
Rezaie et al.29 | Modified golden jackal optimization | Significant improvements in PEMFC parameter modeling | Requires further validation for real-world applicability | Real-world applicability |
Ding et al.30 | Review of machine learning applications | Comprehensive overview of PEMFC optimization | No practical implementation examples | Practical implementation examples |
Yang et al.31 | Bald Eagle Search Algorithm | High efficiency in PEMFC parameter identification | Limited exploration of algorithm performance under diverse conditions | Performance under diverse conditions |
Losantos et al.32 | Genetic algorithms for HTPEMFCs | Focus on high-temperature PEMFCs | Limited insights into standard PEMFCs | Application to standard PEMFCs |
Liao et al.33 | Neural networks for educational evaluation | Limited relevance to PEMFC modeling | Limited relevance to PEMFC modeling | Relevance to PEMFC modeling |
Li et al.34 | Multi-objective deep reinforcement learning | Promising results in PEMFC control | Lack of practical implementation examples | Practical implementation examples |
Abdel-Basset et al.35 | Efficient parameter estimation algorithm | High accuracy in PEMFC parameter estimation | Lack of insights into scalability | Scalability to larger systems |
Li et al.36 | Improved deterministic policy gradient algorithm | Effective results in PEMFC control | Limited adaptability to varied operating conditions | Adaptability to varied conditions |
Gouda et al.37 | Jellyfish Search Algorithm | Significant improvements in PEMFC parameter extraction | Scalability remains a concern | Scalability to larger systems |
Zhu et al.38 | Adaptive Sparrow Search Algorithm | Accuracy in PEMFC parameter identification | Limited experimental validation | Extensive experimental validation |
Alizadeh et al.39 | SCCSA optimization algorithm | Robustness in PEMFC parameter extraction | No exploration of scalability to larger systems | Scalability to larger systems |
