Zga Adel, Zitouni Farouq, Harous Saad, Sallam Karam, Almazyad Abdulaziz S, Xiong Guojiang, Mohamed Ali Wagdy
Department of Computer Science and Information Technology, Laboratory of Artificial Intelligence and Information Technology, Kasdi Merbah University, Ouargla, Algeria.
Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates.
PeerJ Comput Sci. 2025 Jan 27;11:e2646. doi: 10.7717/peerj-cs.2646. eCollection 2025.
This study conducts a comparative analysis of the performance of ten novel and well-performing metaheuristic algorithms for parameter estimation of solar photovoltaic models. This optimization problem involves accurately identifying parameters that reflect the complex and nonlinear behaviours of photovoltaic cells affected by changing environmental conditions and material inconsistencies. This estimation is challenging due to computational complexity and the risk of optimization errors, which can hinder reliable performance predictions. The algorithms evaluated include the Crayfish Optimization Algorithm, the Golf Optimization Algorithm, the Coati Optimization Algorithm, the Crested Porcupine Optimizer, the Growth Optimizer, the Artificial Protozoa Optimizer, the Secretary Bird Optimization Algorithm, the Mother Optimization Algorithm, the Election Optimizer Algorithm, and the Technical and Vocational Education and Training-Based Optimizer. These algorithms are applied to solve four well-established photovoltaic models: the single-diode model, the double-diode model, the triple-diode model, and different photovoltaic module models. The study focuses on key performance metrics such as execution time, number of function evaluations, and solution optimality. The results reveal significant differences in the efficiency and accuracy of the algorithms, with some algorithms demonstrating superior performance in specific models. The Friedman test was utilized to rank the performance of the various algorithms, revealing the Growth Optimizer as the top performer across all the considered models. This optimizer achieved a root mean square error of 9.8602187789E-04 for the single-diode model, 9.8248487610E-04 for both the double-diode and triple-diode models and 1.2307306856E-02 for the photovoltaic module model. This consistent success indicates that the Growth Optimizer is a strong contender for future enhancements aimed at further boosting its efficiency and effectiveness. Its current performance suggests significant potential for improvement, making it a promising focus for ongoing development efforts. The findings contribute to the understanding of the applicability and performance of metaheuristic algorithms in renewable energy systems, providing valuable insights for optimizing photovoltaic models.
本研究对十种新颖且性能良好的元启发式算法在太阳能光伏模型参数估计方面的性能进行了比较分析。该优化问题涉及准确识别反映光伏电池复杂非线性行为的参数，这些行为受环境条件变化和材料不一致性影响。由于计算复杂性和优化误差风险，这种估计具有挑战性，而这些误差可能会阻碍可靠的性能预测。评估的算法包括小龙虾优化算法、高尔夫优化算法、长鼻浣熊优化算法、豪猪优化器、生长优化器、人工原生动物优化器、秘书鸟优化算法、母体优化算法、选举优化算法以及基于技术和职业教育培训的优化器。这些算法被应用于求解四个成熟的光伏模型：单二极管模型、双二极管模型、三二极管模型以及不同的光伏模块模型。该研究聚焦于关键性能指标，如执行时间、函数评估次数和解决方案最优性。结果显示算法在效率和准确性方面存在显著差异，一些算法在特定模型中表现出卓越性能。利用弗里德曼检验对各种算法的性能进行排名，结果表明生长优化器在所有考虑的模型中表现最佳。对于单二极管模型，该优化器的均方根误差为9.8602187789E - 04；对于双二极管模型和三二极管模型，均为9.8248487610E - 04；对于光伏模块模型，为1.230730685 /span>E - 02。这种持续的成功表明，生长优化器是未来旨在进一步提高其效率和有效性的增强改进的有力竞争者。其当前性能表明有显著的改进潜力，使其成为当前开发工作的一个有前景的重点。这些发现有助于理解元启发式算法在可再生能源系统中的适用性和性能，为优化光伏模型提供了有价值的见解。
