Volume 6 • Issue 1 • PP: 94–102 • 2026
Energy Optimization Problems: A Comprehensive Review of Metaheuristic Algorithms and Recent Advances
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Introducing renewable energy into contemporary power systems is crucial to guaranteeing sustainable solutions and improving energy performance. Optimizing energy generation, demand forecasting, and system stability have become difficult with the increasing popularity of renewable energy sources like wind and solar energy systems. This literature review explores recent advances in addressing these challenges by applying artificial intelligence (AI), machine learning (ML), and metaheuristic optimization algorithms. Some of those papers are reviewed because they show advancements in forecasting renewable energy generation, controlling hybrid microgrids, and managing energy in smart grids. Particular attention is given to innovative models such as adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) for wind speed prediction, the Evolutionary Neural Machine Inference Model (ENMIM) for residential energy consumption, and the Wolf-Inspired Optimized Support Vector Regression (WIOSVR) for building energy forecasts. Further, the review discusses the emergence of hybrid renewable energy systems and evaluates advancements in techno-economic optimization. The works under review explore advancements in prediction performance, system availability, and cost, thus making a real contribution to further developing reliable and effective energy systems. Thus, these findings may be used to change to more sustainable energy systems in urban and off-grid environments. It will also lead to further exploration of new optimization techniques and improved synergistic application of renewable energy into electricity networks worldwide.
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References
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