Hybrid Optimization Techniques for Neural Network Training

Abstract

Training neural networks efficiently remains a critical challenge due to issues such as local minima, slow convergence, and sensitivity to initial parameters. Traditional gradient-based optimization techniques, while effective, often struggle with complex, high-dimensional search spaces. This paper proposes a Hybrid Optimization Framework for Neural Network Training that combines metaheuristic algorithms with conventional learning methods to enhance performance and stability. The proposed approach integrates global search capabilities of optimization techniques such as Genetic Algorithms and Particle Swarm Optimization with local fine-tuning using gradient descent. This hybridization enables improved weight initialization, faster convergence, and avoidance of premature stagnation. Experimental evaluation demonstrates that the proposed model achieves higher accuracy and reduced training time compared to standalone optimization methods. Furthermore, the framework shows robustness across different datasets and problem domains. The study builds upon prior research in machine learning and healthcare prediction models, highlighting the effectiveness of hybrid and ensemble-based approaches. The results indicate that hybrid optimization techniques provide a scalable and efficient solution for training deep learning models, making them suitable for real-world applications where accuracy and computational efficiency are critical.