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

doi:10.1038/s41598-025-00594-2

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

2025-05-25

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I

Idriss Dagal *

K

Kemdoum, Fritz Nguemo

Donfack, Emmanuel Fendzi

H

Hassan Abouobaida

DOI：10.1038/s41598-025-00594-2

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This 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.

Keywords:

Logarithmic Mean-Based optimization (LMO)

Global optimization

Renewable energy systems

Photovoltaic optimization

Metaheuristic techniques

Hybrid PV-Wind systems

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

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