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Cited by in F6Publishing
For: Huang Z, Chen L, Li M, Liu PX, Li C. A multiple learning moth flame optimization algorithm with probability-based chaotic strategy for the parameters estimation of photovoltaic models. Journal of Renewable and Sustainable Energy 2021;13:043502. [DOI: 10.1063/5.0048961] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Nagaraju R, Pentang JT, Abdufattokhov S, Cosioborda RF, Mageswari N, Uganya G. Attack prevention in IoT through hybrid optimization mechanism and deep learning framework. Measurement: Sensors 2022;24:100431. [DOI: 10.1016/j.measen.2022.100431] [Reference Citation Analysis]
2 Sharma A, Sharma A, Averbukh M, Rajput S, Jately V, Choudhury S, Azzopardi B. Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module. Energy Reports 2022;8:6576-92. [DOI: 10.1016/j.egyr.2022.05.011] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Arandian B, Eslami M, Khalid SA, Khan B, Sheikh UU, Akbari E, Mohammed AH. An Effective Optimization Algorithm for Parameters Identification of Photovoltaic Models. IEEE Access 2022;10:34069-84. [DOI: 10.1109/access.2022.3161467] [Cited by in F6Publishing: 1] [Reference Citation Analysis]