Dagal, IdrissHarrison, AmbeMbasso, Wulfran FendziKemdoum, Fritz NguemoKhishe, MohammadJangir, PradeepSmerat, Aseel2026-01-312026-01-3120252045-2322https://doi.org./10.1038/s41598-025-00594-2https://hdl.handle.net/20.500.12662/10672This 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.eninfo:eu-repo/semantics/openAccessLogarithmic Mean-Based optimization (LMO)Global optimizationRenewable energy systemsPhotovoltaic optimizationMetaheuristic techniquesHybrid PV-Wind systemsArticle10.1038/s41598-025-00594-22-s2.0-105006469360140414951Q115WOS:001494976800031Q1