1. Lian, J., et al., A review on recent sizing methodologies of hybrid renewable energy systems. Energy Conversion and Management, 2019. 199: p. 112027.
2. Khan, M.J., Review of Recent Trends in Optimization Techniques for Hybrid Renewable Energy System. Archives of Computational Methods in Engineering, 2020: p. 1-11.
3. Muh, E. and F. Tabet, Comparative analysis of hybrid renewable energy systems for off-grid applications in Southern Cameroons. Renewable energy, 2019. 135: p. 41-54.
4. Sadeghi, D., A.H. Naghshbandy, and S. Bahramara, Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization. Energy, 2020. 209: p. 118471.
5. Aljohani, T.M., A.F. Ebrahim, and O. Mohammed, Hybrid Microgrid Energy Management and Control Based on Metaheuristic-Driven Vector-Decoupled Algorithm Considering Intermittent Renewable Sources and Electric Vehicles Charging Lot. Energies, 2020. 13(13): p. 3423.
6. Wu, L., et al., Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction. Computers and Electronics in Agriculture, 2020. 168: p. 105115.
7. Othman, A.M., M.h. Helaimi, and H.A. Gabbar, Enhanced Nature-Inspired Meta-Heuristic Algorithm for Microgrid Performance Improvement. Electric Power Components and Systems, 2020: p. 1-12.
8. Fei, X., R. Xuejun, and N. Razmjooy, Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019: p. 1-21.
9. Fathabadi, H., Novel high efficient speed sensorless controller for maximum power extraction from wind energy conversion systems. Energy Conversion and Management, 2016. 123: p. 392-401.
10. Jackson, P., On the displacement height in the logarithmic velocity profile. Journal of fluid mechanics, 1981. 111: p. 15-25.
11. Gong, W. and N. razmjooy, A new optimisation algorithm based on OCM and PCM solution through energy reserve. International Journal of Ambient Energy, 2020: p. 1-14.
12. Fathabadi, H., Novel high efficiency DC/DC boost converter for using in photovoltaic systems. Solar Energy, 2016. 125: p. 22-31.
13. Gomez-Merchan, R., et al., Binary Search-Based Flexible Power Point Tracking Algorithm for Photovoltaic Systems. IEEE Transactions on Industrial Electronics, 2020.
14. Ott, S., et al., Ionomer distribution control in porous carbon-supported catalyst layers for high-power and low Pt-loaded proton exchange membrane fuel cells. Nature Materials, 2020. 19(1): p. 77-85.
15. Guo, Y., et al., An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application. Energy Reports, 2020. 6: p. 885-894.
16. Zhang, G., et al., Optimal location and size of a grid-independent solar/hydrogen system for rural areas using an efficient heuristic approach. Renewable Energy, 2020.
17. Zhang, G., C. Xiao, and N. Razmjooy, Optimal Parameter Extraction of PEM Fuel Cells by Meta-heuristics. International Journal of Ambient Energy, 2020(just-accepted): p. 1-22.
18. Li, D., et al., Maximum power efficiency operation and generalized predictive control of PEM (proton exchange membrane) fuel cell. Energy, 2014. 68: p. 210-217.
19. Sengupta, S., S. Basak, and R.A. Peters, Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives. Machine Learning and Knowledge Extraction, 2019. 1(1): p. 157-191.
20. Mir, M., et al., Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis. Computational Intelligence, 2020. 36(1): p. 225-258.
21. Shamel, A. and N. Ghadimi, Hybrid PSOTVAC/BFA technique for tuning of robust PID controller of fuel cell voltage. 2016.
22. Razmjooy, N., M. Khalilpour, and M. Ramezani, A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system. Journal of Control, Automation and Electrical Systems, 2016. 27(4): p. 419-440.
23. Razmjooy, N., V.V. Estrela, and H.J. Loschi, Entropy-Based Breast Cancer Detection in Digital Mammograms Using World Cup Optimization Algorithm. International Journal of Swarm Intelligence Research (IJSIR), 2020. 11(3): p. 1-18.
24. Dhiman, G. and V. Kumar, Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 2018. 159: p. 20-50.
25. Baliarsingh, S.K., et al., Analysis of high-dimensional biomedical data using an evolutionary multi-objective emperor penguin optimizer. Swarm and Evolutionary Computation, 2019. 48: p. 262-273.
26. Tang, F., J. Li, and N. Zafetti, Optimization of residential building envelopes using an improved Emperor Penguin Optimizer. Engineering with Computers, 2020: p. 1-13.
27. Arora, S. and S. Singh, Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, 2019. 23(3): p. 715-734.
28. Arora, S. and S. Singh, An improved butterfly optimization algorithm with chaos. Journal of Intelligent & Fuzzy Systems, 2017. 32(1): p. 1079-1088.
29. Xiang, W.-l., et al., A dynamic shuffled differential evolution algorithm for data clustering. Neurocomputing, 2015. 158: p. 144-154.
30. Liang, J.J., B.Y. Qu, and P.N. Suganthan, Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 2013. 635.
31. Wang, P.C. and T.E. Shoup, A poly-hybrid PSO optimization method with intelligent parameter adjustment. Advances in Engineering Software, 2011. 42(8): p. 555-565.
32. Rashedi, E., H. Nezamabadi-Pour, and S. Saryazdi, GSA: a gravitational search algorithm. Information sciences, 2009. 179(13): p. 2232-2248.
33. Dhiman, G. and V. Kumar, Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 2017. 114: p. 48-70.
34. Amali, D. and M. Dinakaran, Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour. Journal of Intelligent & Fuzzy Systems, 2019(Preprint): p. 1-14.
35. HOMER, P., NASA surface meteorology and solar energy database. 2019.