Logarithmic mean optimization a metaheuristic algorithm for global and case specific energy optimization

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dc.authorid0000-0001-6944-4775
dc.authorid0000-0002-4049-0716
dc.authorid0000-0002-4353-1261
dc.authorid0000-0002-1024-8822
dc.contributor.authorDagal, Idriss
dc.contributor.authorHarrison, Ambe
dc.contributor.authorMbasso, Wulfran Fendzi
dc.contributor.authorKemdoum, Fritz Nguemo
dc.contributor.authorKhishe, Mohammad
dc.contributor.authorJangir, Pradeep
dc.contributor.authorSmerat, Aseel
dc.date.accessioned2026-01-31T15:08:23Z
dc.date.available2026-01-31T15:08:23Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractThis study introduces a novel metaheuristic optimization algorithm named Logarithmic Mean-Based Optimization (LMO), designed to enhance convergence speed and global optimality in complex energy optimization problems. LMO leverages logarithmic mean operations to achieve a superior balance between exploration and exploitation. The algorithm's performance was benchmarked against six established methods-Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), Cuckoo Search Algorithm (CSA), and Firefly Algorithm (FA)-using the CEC 2017 suite of 23 high-dimensional functions. LMO achieved the best solution on 19 out of 23 benchmark functions, significantly outperforming all comparison algorithms. It demonstrated a mean improvement of 83% in convergence time and up to 95% better accuracy in optimal values over competitors. In a real-world application, LMO was employed to optimize a hybrid photovoltaic (PV) and wind energy system, achieving a 5000 kWh energy yield at a minimized cost of $20,000, outperforming all other algorithms in both efficiency and effectiveness. The results affirm LMO's capability for robust, scalable, and cost-effective optimization in renewable energy systems.
dc.description.sponsorshipDeanship of Research and Graduate Studies at King Khalid University [RGP2/108/46]
dc.description.sponsorshipThe authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Small Research Project under grant number RGP2/108/46.
dc.identifier.doi10.1038/s41598-025-00594-2
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.pmid40414951
dc.identifier.scopus2-s2.0-105006469360
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org./10.1038/s41598-025-00594-2
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10672
dc.identifier.volume15
dc.identifier.wosWOS:001494976800031
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_20260128
dc.subjectLogarithmic Mean-Based optimization (LMO)
dc.subjectGlobal optimization
dc.subjectRenewable energy systems
dc.subjectPhotovoltaic optimization
dc.subjectMetaheuristic techniques
dc.subjectHybrid PV-Wind systems
dc.titleLogarithmic mean optimization a metaheuristic algorithm for global and case specific energy optimization
dc.typeArticle

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