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 often struggle with complex and high-dimensional search spaces. This paper presents 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 techniques such as Genetic Algorithms and Particle Swarm Optimization with local fine-tuning using gradient descent. This hybrid strategy improves weight initialization, accelerates convergence, and avoids premature stagnation. Experimental results demonstrate that the proposed model achieves higher accuracy and reduced training time compared to standalone optimization methods. Additionally, integration with prior research in machine learning and healthcare analytics enhances robustness and generalization. The study highlights the effectiveness of hybrid optimization techniques in developing efficient and scalable neural network models for real-world applications.