aspg-28-4509-1792155046 20261016125046 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2026 11 1 Alaa Mohamed Abdel-Moati Department of Architecture Engineering, Delta Higher Institute for Engineering & Technology (DHIET), Mansoura, Egypt Abdullah Muhammad Ibrahim Department of Civil Engineering, Delta Higher Institute for Engineering & Technology (DHIET), Mansoura, Egypt Accurate solar radiation prediction is critical for optimizing renewable energy utilization, yet traditional machinelearning models struggle with suboptimal forecasting accuracy. To address this challenge, we propose a novel hybrid optimization algorithm, the Grey Wolf and Water Whale Plant Optimizer (GWWWPA), to enhance Long Short-Term Memory (LSTM) networks for improved solar radiation forecasting. The proposed method leverages the exploration-exploitation synergy of the Grey Wolf Optimizer (GWO) and Water Whale Plant Algorithm (WWPA) to optimize LSTM hyperparameters effectively. Experimental results on the NASA Space Apps Moscow dataset demonstrate that GWWWPA-LSTM outperforms existing optimization techniques, achieving the lowest Mean Squared Error (MSE) of 0.00019392 and the highest R2 score of 0.917262, surpassing standalone GWO, WWPA, PSO, and WOA. These findings highlight the potential of hybrid metaheuristic approaches in enhancing predictive accuracy, facilitating more reliable solar energy forecasting, and supporting sustainable energy management systems. 2026 56 67 4509 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.110106 https://www.americaspg.com/journal/28/article/4509 https://www.americaspg.com/storage/articles/manuscripts/01M2ZKK5D0JVXCY627KDJ0QKQE.pdf