When it comes to identifying the industrial solutions of solar photovoltaic (PV) systems, one of the difficult tasks is predicting the effectiveness of a PV system since the P-V, and I-V features of a PV system are no...
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When it comes to identifying the industrial solutions of solar photovoltaic (PV) systems, one of the difficult tasks is predicting the effectiveness of a PV system since the P-V, and I-V features of a PV system are nonlinear in nature. It is important to evidence that most manufacturers' specification sheets do not include comprehensive data about the equivalent circuit variables of PV models, which is essential to simulate an accurate solar module. Contrasted to other methods of extracting parameters from solar PV cells/modules, global research methodologies, and metaheuristic optimizationalgorithms are highly suitable as the main substitute for parameter extraction. Accordingly, this study proposes a new optimization approach for extracting the characteristics of solar PV cells/modules of various models, such as single-diode, double-diode, and module models, while accurately depicting the I-V and P-V curves. This is accomplished by using a chaotic generator in conjunction with the recently reported jumping spider optimization algorithm (JSOA) to obtain PV parameters from the optimization process. Comparative performance analysis reveals that the suggested optimization method, called CJSOA using a chaotic-based search sequence, performs better than many state-of-the-art optimizationalgorithms in terms of precision and reliability when identifying the PV parameters, as demonstrated in this paper. With the average Friedman's ranking test value of 1.397, the proposed CJSOA stood first among all selected algorithms.
The massive influx of traffic on the Internet has made the composition of web traffic increasingly *** port-based or protocol-based network traffic identification methods are no longer suitable for today’s complex an...
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The massive influx of traffic on the Internet has made the composition of web traffic increasingly *** port-based or protocol-based network traffic identification methods are no longer suitable for today’s complex and changing ***,machine learning has beenwidely applied to network traffic ***,high-dimensional features and redundant data in network traffic can lead to slow convergence problems and low identification accuracy of network traffic recognition *** advantage of the faster optimizationseeking capability of the jumping spider optimization algorithm(JSOA),this paper proposes a jumping spider optimization algorithmthat incorporates the harris hawk optimization(HHO)and small hole imaging(HHJSOA).We use it in network traffic identification feature ***,the method incorporates the HHO escape energy factor and the hard siege strategy to forma newsearch strategy for *** location update strategy enhances the search range of the optimal solution of *** use small hole imaging to update the inferior ***,the feature selection problem is coded to propose a jumpingspiders individual coding *** iterations of the HHJSOA algorithmfind the optimal individual used as the selected feature for KNN ***,we validate the classification accuracy and performance of the HHJSOA algorithm using the UNSW-NB15 dataset and KDD99 *** results show that compared with other algorithms for the UNSW-NB15 dataset,the improvement is at least 0.0705,0.00147,and 1 on the accuracy,fitness value,and the number of *** addition,compared with other feature selectionmethods for the same datasets,the proposed algorithmhas faster convergence,better merit-seeking,and ***,HHJSOAcan improve the classification accuracy and solve the problem that the network traffic recognition algorithm needs to be faster to converge and easily fall into local op
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