Optimization is critical for optimal performance in deep neural networks (DNNs). Traditional gradient-based methods often face challenges like local minima entrapment. This paper explores population-based metaheuristic optimization algorithms for image classification networks. We propose a novel approach integrating a two-stage training technique with population-based optimization algorithms incorporating local search capabilities. Our experiments demonstrate that the proposed method outperforms state-of-the-art gradient-based techniques, such as ADAM, in accuracy and computational efficiency, particularly with high computational complexity and numerous trainable parameters. The results suggest that our approach offers a robust alternative to traditional methods for weight optimization in convolutional neural networks (CNNs). Future work will explore integrating adaptive mechanisms for parameter tuning and applying the proposed method to other types of neural networks and real-time applications.

本研究针对深度神经网络在优化过程中面临的局部最优陷阱问题，提出了一种结合两阶段训练技术与群体优化算法的创新方法，支持局部搜索能力。实验结果表明，该方法在准确性和计算效率上优于传统的基于梯度的优化技术，尤其在计算复杂度高和可训练参数众多的情况下，展现出对卷积神经网络权重优化的强大替代能力。

利用拉马克式记忆算法和局部搜索增强卷积神经网络分类