ArticleScientific reports2025
Logarithmic mean optimization a metaheuristic algorithm for global and case specific energy optimization.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Beyond Bio-Inspired Algorithms: Using Bird Landing Dynamics to Design Adaptive Parameter Control in Metaheuristics.Biomimetics (Basel, Switzerland) · 2026Article
- An enhanced energy management framework based on artificial gorilla troops for optimal operation of grid-connected multi-nanogrids.Scientific reports · 2026Article
- Optimal design and operation of grid-connected hybrid microgrid system using pelican optimization algorithm.Scientific reports · 2026Article
- An innovative metaheuristic algorithm for photovoltaic tilt angle optimization.Scientific reports · 2025Article
- Swift Flight Optimizer: a novel bio-inspired optimization algorithm based on swift bird behavior.Scientific reports · 2025Article
- A dual enhanced stochastic gradient descent method with dynamic momentum and step size adaptation for improved optimization performance.Scientific reports · 2025Article
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Authors and funding
11 authors.
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Abstract
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.
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