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Status: Unchecked ×Keyword: double diode model ×Clear all

3 claims from 1 paper

1. Computer Science › Metaheuristic Optimization Algorithms Research

   Elite Bernoulli-based mutated dung beetle algorithm for global complex problems and parameter estimation of solar photovoltaic models

   Elhoseny, Abdel-Salam, Nayyar et al. · Scientific Reports · 2025

   The paper presents EBMLO-DBO, an improved dung beetle optimiser, and reports that it beats other algorithms on benchmark functions and on parameter estimation for solar photovoltaic models.

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   1. UncheckedOn the CEC2017 and CEC2022 benchmark suites, the new EBMLO-DBO algorithm gets Friedman ranks of 1.83 and 2.7, ahead of eleven other algorithms.“The performance of EBMLO-DBO is rigorously validated using the CEC2017 and CEC2022 benchmark suites, where it achieves Friedman ranks of 1.83 and 2.7 respectively, consistently surpassing eleven state-of-the-art algorithms including PSO, HHO, WOA, and advanced methods like CMAES and IMODE.”
   2. UncheckedAn improved dung beetle optimiser reports low error when fitting solar cell and module models, with top average ranks of 1.45, 1.42 and 1.74.“For photovoltaic parameter estimation applications, EBMLO-DBO exhibits exceptional accuracy with RMSE values of 9.8602E-4 for single diode models, 9.81307E-4 for double diode models, and 2.32066E-3 for PV module models, achieving top performance ranks of 1.45, 1.42, and 1.74, respectively.”
   3. UncheckedA Wilcoxon signed-rank test at the 0.05 level shows the new EBMLO-DBO algorithm significantly outperforming all the algorithms it was compared against.“Statistical analysis using Wilcoxon signed-rank test at significance level $$\alpha =0.05$$ confirms the significant superiority of EBMLO-DBO over all compared algorithms, thereby validating the effectiveness and reliability of the proposed enhancements.”