Parameter extraction of photovoltaic (PV) models based on measured current–voltage data plays an important role in the control, simulation, and optimization of PV systems. Despite the fact that various parameter extraction strategies have been dedicated to solving this problem, they may have certain drawbacks. In this paper, an effective hybrid optimization method based on adaptive rat swarm optimization (ARSO) and pattern search (PS) is presented for effectively and consistently extracting PV parameters. The proposed method employs the global search ability of ARSO and the local search ability of PS. The performance of the new algorithm is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. The extraction of parameters from several PV models, such as single‐diode, double‐diode, and PV modules, confirms the performance of the suggested method. Simulation results show that the proposed method surpasses other state‐of‐the‐art procedures in terms of accuracy, reliability, and convergence speed.

At present, the existing prediction algorithm of a port cargo throughput neglects the correction of the initial value of the cargo series data model, which leads to a large error in a port cargo throughput prediction. Therefore, a prediction algorithm of a port cargo throughput based on the ES‐Markov model is designed. A decompose function is used to decompose the time series of a port cargo throughput, and the trend elements of a port cargo throughput are divided into long‐term trend, seasonal trend, fluctuation trend, and irregular trend. In this study, the ES‐Markov model is introduced, and the initial prediction is obtained by using the cubic exponential smoothing method, and the state transition matrix is obtained by the Markov principle. Based on the results of the time‐series analysis and the ES‐Markov model, the prediction algorithm of a port cargo throughput is designed. In the experimental design, the Elman neural network is used to construct an experimental sample data model. The monthly cargo throughput data of a certain port for eight months from May 2020 to December 2020 are collected and sorted according to the time series. The experimental results show that the prediction results of the proposed algorithm are closer to the actual value and the fluctuation of the prediction results is less than that of the reference.

Since Wikipedia encyclopedia is one of the most popular web sites on the internet, providing accurate information is of abundant importance. In this research, the effective variables on quality of Persian articles are identified and a system is, then, designed for judging articles in three quality levels: high quality, cleanup needed, and deletion. First, the variables relating to the articles included in the list of featured articles, good articles, cleanup needed, and deletion articles are collected. Then, two methods are used for the analysis of data: First, a decision tree explains the relationships among the collected variables as rules that are implemented by adaptive neuro fuzzy interference system. Second, the data are implemented by subtractive clustering algorithm and the error of both methods is, finally, measured and compared. The results indicate that the average daily hits, total views, page length, total number of edits, total number of authors, and number of templates used are directly related to quality of Persian articles while the number of recent number of authors is inversely related to quality of articles.