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

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dc.contributor.authorElhosseny, Mohamed
dc.contributor.authorAbdel-Salam, Mahmoud
dc.contributor.authorNayyar, Anand
dc.contributor.authorCelik, Emre
dc.contributor.authorMahajan, Shubham
dc.contributor.authorBacanin, Nebojsa
dc.contributor.authorEl-Hasnony, Ibrahim M.
dc.date.accessioned2026-07-01T11:39:55Z
dc.date.available2026-07-01T11:39:55Z
dc.date.issued2025
dc.departmentDüzce Üniversitesi
dc.description.abstractThe Dung Beetle Optimization (DBO) algorithm is a relatively recent metaheuristic known for its simplicity, versatility, and low parameter dependence, making it a valuable tool for solving complex optimization problems. Despite its potential, DBO suffers from limitations such as slow convergence and premature stagnation in local optima. To address these critical issues, this paper introduces a novel enhanced variant named Elite Bernoulli-based Mutated Dung Beetle Optimizer with Local Escaping Operator (EBMLO-DBO), specifically designed to improve the convergence speed, search capability, and robustness of the original DBO algorithm. The motivation for this enhancement stems from DBO's limited performance in high-dimensional and non-convex problems, where it often fails to maintain an effective balance between exploration and exploitation. The novelty of the proposed EBMLO-DBO lies in the integration of four key strategies tailored to overcome these weaknesses: (i) Bernoulli map-based initialization to enhance population diversity and ensure a better global search foundation; (ii) Morlet Wavelet mutation to introduce adaptive local refinements and help the algorithm escape local optima; (iii) elite guidance to accelerate convergence by directing the population toward high-quality regions; and (iv) a local escaping operator (LEO) to dynamically refine the search process and strengthen exploitation without sacrificing exploration. 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. In benchmark function optimization, EBMLO-DBO demonstrates superior performance by achieving first rank in 50% of CEC2022 functions and obtaining the lowest average fitness values in 18 out of 29 CEC2017 functions. 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. Statistical analysis using Wilcoxon signed-rank test at significance level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha =0.05$$\end{document} confirms the significant superiority of EBMLO-DBO over all compared algorithms, thereby validating the effectiveness and reliability of the proposed enhancements. Overall, the results state that EBMLO-DBO offers a significantly improved search performance and solution quality compared to the original DBO and related methods, thereby justifying the necessity and effectiveness of the proposed enhancements.
dc.identifier.doi10.1038/s41598-025-06108-4
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.orcid0000-0002-9714-0717
dc.identifier.orcid0000-0002-2062-924X
dc.identifier.pmid41107273
dc.identifier.scopus2-s2.0-105019114779
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1038/s41598-025-06108-4
dc.identifier.urihttps://hdl.handle.net/20.500.12684/23546
dc.identifier.volume15
dc.identifier.wosWOS:001597098000015
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260623
dc.subjectBernoulli Operator
dc.subjectMorlet Wavelet
dc.subjectDung Beetle Optimization Algorithm
dc.subjectSolar Photovoltaic Parameter Estimation
dc.subjectGlobal Optimization
dc.titleElite Bernoulli-based mutated dung beetle algorithm for global complex problems and parameter estimation of solar photovoltaic models
dc.typeArticle

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