Abstract An improved Harris hawks optimization is proposed in this work to facilitate the simulation of an efficient photovoltaic system and extraction of unknown parameters, which combines horizontal and vertical crossover mechanism of the crisscross optimizer and Nelder-Mead simplex algorithm, named CCNMHHO. In CCNMHHO, the cores appeared in the crisscross optimizer are utilized to enrich the information exchange between the individuals and avoid the problem of dimensional stagnation of individuals all through the iterations. Hence, it enhances to change to improve the population quality and prevent the shortcoming of falling into a local optimum. In contrast, the Nelder-Mead simplex algorithm is employed in the proposed CCNMHHO methodology. Nelder-Mead simplex helps to improve individual searching capabilities in performing the local search phase and showing a faster convergence to optimal values. Compared to some algorithms that have a competitive performance in dealing with this type of problem, CCNMHHO has a faster convergence speed, and it shows high stability. In different environments, the experimental data obtained by this improved Harris hawks Optimization can reveal a high agreement with the measurement data. The experimental results show that the proposed method not only is very competitive in extracting the unknown parameters of different PV models compared to other state-of-the-art algorithms but also perform well in dealing with the complex outdoor environments such as different temperature and radiance. Therefore, we observed that the CCNMHHO could be considered as a reliable and efficient method in solving a class of cases for the assessment of unknown parameters of solar cells and photovoltaic models. For post-publication guidance, supports, and materials for this research, please refer to the supporting homepage: http://aliasgharheidari.com .

中文翻译：

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Harris hawk 优化器与 Nelder-Mead 单纯形的水平和垂直交叉，用于光伏模型的参数估计

摘要 为了便于高效光伏系统的模拟和未知参数的提取，本文提出了一种改进的Harris hawks优化，它结合了交叉优化器和Nelder-Mead单纯形算法的水平和垂直交叉机制，命名为CCNMHHO。在CCNMHHO中，利用交叉优化器中出现的核来丰富个体之间的信息交换，避免个体在整个迭代过程中维度停滞的问题。因此，加强变化以提高人口质量，防止陷入局部最优的缺点。相比之下，建议的 CCNMHHO 方法中采用了 Nelder-Mead 单纯形算法。Nelder-Mead 单纯形有助于提高执行局部搜索阶段的个人搜索能力，并显示更快地收敛到最佳值。与一些在处理此类问题上具有竞争力的算法相比，CCNMHHO 具有更快的收敛速度，并且表现出较高的稳定性。在不同环境下，这种改进的 Harris hawks 优化得到的实验数据可以揭示与测量数据的高度一致性。实验结果表明，与其他最先进的算法相比，所提出的方法不仅在提取不同光伏模型的未知参数方面极具竞争力，而且在处理不同温度和辐射等复杂室外环境方面也表现良好。 . 所以，我们观察到，CCNMHHO 可以被认为是解决一类用于评估太阳能电池和光伏模型未知参数的案例的可靠和有效的方法。有关本研究的出版后指导、支持和材料，请参阅支持主页：http://aliasgharheidari.com。