Hybrid Swarm Intelligence-Based Neural Framework for Optimizing Real-Time Computational Models in Engineering Systems

In modern engineering systems, real-time computational models are essential for optimizing performance, enhancing decision-making, and reducing latency in complex environments. This research presents a Hybrid Swarm Intelligence-Based Neural Framework (HSIN-F) to improve the efficiency, accuracy, and adaptability of real-time engineering computations. The proposed framework integrates Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Ant Colony Optimization (ACO) with a Deep Neural Network (DNN) to achieve a balance between exploration and exploitation, enabling optimal model parameter selection and reducing computational overhead. To validate the efficiency of HSIN-F, experiments were conducted across various real-time engineering applications, including industrial automation, smart grids, and IoT-based systems. The proposed model outperformed conventional optimization techniques in terms of processing speed, predictive accuracy, and system adaptability. Key performance metrics include: Prediction Accuracy: 98.2% (compared to 93.5% in traditional models), Computational Latency Reduction: 34.7%, Energy Efficiency Improvement: 27.5%, Error Rate Reduction: 32.1%. The hybrid swarm-based approach effectively adapts to dynamic changes in real-time scenarios, making it highly suitable for engineering applications requiring continuous optimization. Future research will explore hybrid metaheuristic strategies and federated learning-based decentralization to further enhance system performance and robustness.