Dynamic-fitness-distance-balance stochastic fractal search (dFDB-SFS algorithm): an effective metaheuristic for global optimization and accurate photovoltaic modeling

The stochastic fractal search (SFS) algorithm, among population-based metaheuristic automation algorithms, is a robust optimization algorithm for solving optimization problems in different fields of science, inspired by the diffusion feature and natural growth phenomenon seen regularly in random fra...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 28; H. 9-10; S. 6447 - 6474
Hauptverfasser: Kahraman, Hamdi Tolga, Hassan, Mohamed H., Katı, Mehmet, Tostado-Véliz, Marcos, Duman, Serhat, Kamel, Salah
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Alternative energy sources
Artificial Intelligence
Computational Intelligence
Control
Design
Engineering
Exploitation
Fractals
Global optimization
Heuristic
Heuristic methods
Mathematical Logic and Foundations
Mechatronics
Methods
Optimization
Optimization algorithms
Parameter estimation
Parameters
Performance evaluation
Photovoltaic cells
Robotics
Root-mean-square errors
Searching
Statistical analysis
PV parameters estimation
Renewable energy
FDB-SFS algorithm
Optimization
ISSN:1432-7643, 1433-7479
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Zusammenfassung:The stochastic fractal search (SFS) algorithm, among population-based metaheuristic automation algorithms, is a robust optimization algorithm for solving optimization problems in different fields of science, inspired by the diffusion feature and natural growth phenomenon seen regularly in random fractals. However, as in population-based optimization algorithms, it is a great challenge to effectively design the selection process in the SFS method. To imitate the selection process in nature effectively and accurately, the dynamic-fitness-distance-balance (dFDB) selection method has been used in the SFS algorithm in six different versions. In this way, the dFDB-SFS algorithm has been developed, which more effectively mimics nature with exploitation, exploration, and balanced search capabilities. Firstly, the performance of the proposed dFDB-SFS algorithm was investigated in CEC 2020 benchmark test functions. Wilcoxon and Friedman statistical analyses of the results obtained from the test functions were made and according to these analysis results, the best version of the proposed approach was determined. Secondly, the performance of the proposed algorithm was investigated in determining photovoltaic module parameters, which is one of the real-world engineering problems. In this article, the dFDB-SFS algorithm uses the root mean square error (RMSE) as the objective function to estimate the unknown parameters of the single diode model (SDM), double diode model (DDM), and PV module models. In terms of a quantitative and qualitative performance evaluation, it reveals that the proposed algorithm provides better results than other proposed algorithms in terms of accuracy and robustness when obtaining PV parameters.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-09505-x