According to the manufacturer’s - data, we need to obtain the best parameters for assessing the photovoltaic systems. Although much work has been done in this area, it is still challenging to extract model parameters accurately. An efficient solver called SFLBS is developed to deal with this problem, in which an inheritance mechanism based on crossover and mutation is introduced. Specifically, the memory pool for storing historical population information is designed. During the sub-population evolution, the historical population will cross and mutate with the contemporary population with a certain probability, ultimately inheriting information about the dimensions that perform well. This mechanism ensures the population’s quality during the evolution process and effectively improves the local search ability of traditional SFLA. The proposed SFLBS is applied to extract unknown parameters from the single diode model, double diode model, three diode model, and photovoltaic module model. Based on the experimental results, we found that SFLBS has considerable accuracy in extracting the unknown parameters of the PV system problem, and its convergence speed is satisfactory. Moreover, SFLBS is used to evaluate three commercial PV modules under different irradiance and temperature conditions. The experimental results demonstrate that the performance of SFLBS is outstanding compared to some state-of-the-art competing algorithms. Moreover, SFLBS is still a reliable optimization tool despite the complex external environment. This research is supported by an online service for any question or needs to supplementary materials at .

中文翻译：

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进化洗牌青蛙跳跃与内存池参数优化

根据制造商的数据，我们需要获得评估光伏系统的最佳参数。尽管在这方面已经做了很多工作，但准确提取模型参数仍然具有挑战性。为了解决这个问题，开发了一种名为 SFLBS 的高效求解器，其中引入了基于交叉和变异的继承机制。具体来说，设计了用于存储历史人口信息的内存池。在子种群演化过程中，历史种群会以一定概率与当代种群发生交叉变异，最终继承表现良好的维度信息。该机制保证了进化过程中的种群质量，有效提高了传统SFLA的局部搜索能力。所提出的 SFLBS 用于从单二极管模型、双二极管模型、三二极管模型和光伏组件模型中提取未知参数。基于实验结果，我们发现SFLBS在提取光伏系统问题的未知参数方面具有相当的精度，并且收敛速度令人满意。此外，SFLBS 用于在不同辐照度和温度条件下评估三种商用光伏组件。实验结果表明，与一些最先进的竞争算法相比，SFLBS 的性能非常出色。而且，尽管外部环境复杂，SFLBS仍然是一个可靠的优化工具。这项研究由在线服务提供支持，可解决任何问题或需要补充材料。