Mating-based Sine Cosine Algorithm for Modelling a Solar Photovoltaic Cell
Keywords:
Photovoltaic cell, renewable energy, sine-cosine algorithm, global optimization, system identificationAbstract
This paper presents a mating-based sine-cosine algorithm (MSCA) for solving global optimization problems with an application to optimize the static model of a solar photovoltaic (PV) cell. It is an improved variant of the sine-cosine algorithm (SCA). The original SCA relies heavily on its sine-cosine position update equation formulated based on elitism and random techniques. Euclidean distance between an agent to the elite agent provides a good searching strategy as it keeps track of the current best solution. However, solely relying on the equation has led to a premature stagnation among the search agents and thus yielding a local optimum solution. A mating-based technique is introduced to enhance the exploration of the searching agents and their motion on the feasible region. Some good features of the elite agent are shared with other agents thereby improving their traits. With the new characteristics, the search agents explore more diversely thereby producing a more promising path. The algorithm is tested on several real-parameter benchmark functions. It is also applied to optimize the static model of a solar PV cell based on a single Current-Voltage (I-V) curve approach, a pair of electrical signals captured from real system. The result on benchmark function test shows that the proposed MSCA has gained greater accuracy. Wilcoxon signed-rank test shows that the two-tailed p-value is less than 0.05 implying the improvement is significant. On the other hand, the result on the static modeling of solar PV cell shows both algorithms have satisfactorily acquired a good model. The MSCA attained a better accuracy than the SCA algorithm and state-of-the-art spiral dynamic algorithm (SDA).References
[1] S. A. AL Dawsari, F. Anayi and M. Packianather, Optimizing a hybrid off-grid photovoltaic/wind/fuel cell energy system using mantis search algorithm, Energy Conversion and Management, 27, 2025, 101209.
[2] B. Yang, R. Zheng, Y. Qian, B. Liang and J. Wang, Efficient identification of photovoltaic cell parameters via Bayesian neural network-artificial ecosystem optimization algorithm, Global Energy Interconnection, 8, 2025, 316–337.
[3] C. Mai, L. Zhang and X. Hu, An adaptive snake optimization algorithm incorporating subtraction-average-based optimizer for photovoltaic cell parameter identification, Heliyon, 10, 2024, e35382.
[4] L. Wang, Q. Yuan, B. Zhao, B. Zhu and X. Zeng, Parameter identification of photovoltaic cells/modules by using an improved artificial ecosystem optimization algorithm and Newton-Raphson method, Alexandria Engineering Journal, 123, 2025, 559–591.
[5] W. Li, J. Yang, H. Wang and Y. Wang, EMOCSO: an efficient multi-objective competitive swarm optimizer for large-scale optimization, Expert Systems with Applications, 299, 2026, 130060.
[6] S. Ganguli, S. Goyal and P. Nijhawan, Development of new bioinspired hybrid algorithms for parameter modeling of photovoltaic panels, Linear and Nonlinear System Modeling (Eds. T. Roy, S. I. Tripathi and S. Ganguli), 2024, 89–108.
[7] Y. Wang, H. Chen and W. Cheng, A multi-strategy improved nutcracker optimization algorithm for parameter extraction problems of solar photovoltaic cells, Results in Engineering, 27, 2025, 106868.
[8] A. N. K. Nasir, M. F. M. Jusof, M. R. Ahmad and A. I. Hashim, Spiral-Sooty tern optimization algorithm for dynamic modelling of a twin rotor system, Proceedings of Innovations in Intelligent Systems and Applications Conference (ASYU), Antalya, Turkey, 2022, 1–6.
[9] M. S. Islam and M. A. Ahmad, Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm, Results in Engineering, 24, 2024, 103357.
[10] E. Segerstrom, M. Podlaski, A. Khare and L. Vanfretti, Parameter optimization and model validation of quanser AERO using Modelica and RaPId, Proceedings of AIAA Propulsion and Energy Forum, Denver, Colorado, USA, 2021, 2021–3286.
[11] T. A. Z. Rahman, Parametric modelling of twin rotor system using chaotic fractal search algorithm, Proceedings of 7th IEEE Control and System Graduate Research Colloquium (ICSGRC), Shah Alam, Malaysia, 2016, 34–39.
[12] A. N. K. Nasir and M. O. Tokhi, A novel hybrid bacteria-chemotaxis spiral-dynamic algorithm with application to modelling of flexible systems, Engineering Applications of Artificial Intelligence, 33, 2014, 31–46.
[13] W. Sun, Y. Jin, D. Kai, Z. Guo and F. Ma, Flexible manipulator trajectory tracking based on an improved adaptive particle swarm optimization algorithm with fuzzy PD control, Mechanical Sciences, 16, 2025, 125–141.
[14] Y. Yang, P. Geng, Y. He, W. Bao and L. Zhu, Inverse kinematics modeling of soft manipulator based on optimized particle swarm optimization-back propagation neural network, IEEE/ASME Transactions on Mechatronics, 30, 2025, 5468–5477.
[15] A. A. A. Hashim, N. M. A. Ghani, S. Ahmad and A. N. K. Nasir, System Identification and control of linear electromechanical actuator using PI controller based metaheuristic approach, Applications of Modelling and Simulation, 8, 2024, 213–224.
[16] B. Özkaya, Optimal Parameter estimation of PEMFC Model using an improved atomic orbital search algorithm, Applications of Modelling and Simulation, 8, 2024, 283–300.
[17] A. N. K. Nasir, M. F. M. Jusof, M. R. Ahmad, A. A. A. Razak and N. A. Ghani, Adaptive levy flight distribution algorithm for solving a dynamic model of an electric heater, Proceedings of IEEE 13th Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, Malaysia, 2023, 140–144.
[18] F. Belabbes, D. T. Cotfas, P. A. Cotfas and M. Medles, Using the snake optimization metaheuristic algorithms to extract the photovoltaic cells parameters, Energy Conversion and Management, 292, 2023, 117373.
[19] A. Harraga and S. Messalti, Three, five and seven PV model parameters extraction using PSO, Energy Procedia, 119, 2017, 767–774.
[20] I. H. Smaili, G. Moustafa, D. R. Almalawi, A. Ginidi, A. M. Shaheen and H. S. E. Mansour, Enhanced artificial rabbits algorithm integrating equilibrium pool to support PV power estimation via module parameter identification, International Journal of Energy Research, 2024, 2024, 8913560.
[21] H. Sheng, C. Li, H. Wang, Z. Yan, Y. Xiong, Z. Cao and Q. Kuang, Parameters extraction of photovoltaic models using an improved moth-flame optimization, Energies, 12, 2019, 3527.
[22] S. A. Celtek, S. Kul, M. K. Singla, J. Gupta, M. Safaraliev and H. Zeinoddini-Meymand, Grey wolf-based heuristic methods for accurate parameter extraction to optimize the performance of PV modules, IET renewable power generation, 18, 2024, 1–13.
[23] A. Al-Subhi, M. I. Mosaad and T. A. Farrag, PV parameters estimation using optimized deep neural networks, Sustainable Computing: Informatics and Systems, 41, 2024, 100960.
[24] U. Chauhan, H. Chhabra, P. Jain, A. Dev, N. Chauhan and B. Kumar, Chaos inspired invasive weed optimization algorithm for parameter estimation of solar PV models, IFAC Journal of Systems and Control, 27, 2024, 100239.
[25] H. Krishnan, M. S. Islam, M. A. Ahmad and M. I. M. Rashid, Parameter identification of solar cells using improved Archimedes Optimization Algorithm, Optik, 295, 2023, 171465.
[26] H. Rezk, T. S. Babu, M. Al-Dhaifallah and H. A. Ziedan, A robust parameter estimation approach based on stochastic fractal search optimization algorithm applied to solar PV parameters, Energy Reports, 7, 2021, 620–640.
[27] D. Lei, L. Cai, F. Wu and Y. Tang, Parameter identification of photovoltaic cell using modified bare-bones imperialist competition algorithm, Optik - International Journal for Light and Electron Optics, 305, 2024, 171798.
[28] M. Abderafi, M. El Ghazouali and M. Bouzi, Parameter identification of solar photovoltaic cells using an advanced metaheuristic optimization framework, Energy Conversion and Management, 258, 2022, 115512.
[29] S. P. Adam, A. C. Stamou and M. N. Vrahatis, No free lunch theorem: A review on theorems and their contribution to modern optimization, Swarm and Evolutionary Computation, 50, 2019, 100564.
[30] A. Faramarzi, M. Heidarinejad, S. Mirjalili and A. H. Gandomi, Marine predators algorithm: A novel nature-inspired metaheuristic, Expert Systems with Applications, 152, 2020, 113377.
[31] A. A. Hadi, A. W. Mohamed and K. M. Jambi, Single objective real-parameter numerical optimization: A survey of the state-of-the-art algorithms on CEC2014 benchmark suite, Computational Intelligence and Neuroscience, 2019, 2019, 1–19.
[32] J. Vahidi and S. M. Golmaei, Optimizing construction scheduling using the sine cosine algorithm (SCA) with building information modeling (BIM), Results in Engineering, 28, 2025, 107100.
[33] S. Mirjalili, SCA: A sine cosine algorithm for solving optimization problems, Knowledge-based Systems, 96, 2016, 113–124.
[34] J. J. Liang, Y. Q. Bo and P. N. Suganthan, Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Opt, Technical Report, Nanyang Technological University, Singapore, 635, 2013, 490.
[35] T. Easwarakhanthan, J. Bottin, I. Bouhouch and C. Boutrit, Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers, International Journal of Solar Energy, 4, 1986, 1–12.
[36] K. Tamura and K. Yasuda, Spiral multipoint search for global optimization, Proceedings of Fourth International Conference on Machine Learning and Applications, Honolulu, Hawaii USA, 2011, 470–475.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Mohd Falfazli Mat Jusof, Ahmad Nor Kasruddin Nasir, Ikhwan Hafiz Muhamad, Heru Supriyono
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:Authors hold and retain copyright, and grant the journal right of first publication, with the work after publication simultaneously licensed under a Creative Commons Attribution 4.0 License CC BY that permits any use, reproduction and distribution of the work and article without further permission provided that the original work is properly cited.
Authors are permitted and encouraged to post their work online in institutional repositories, website and other social media before and after publication, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
