Integration of Ant Colony Algorithm with BP Neural Network

In this research, we successfully integrated the Ant Colony Optimization algorithm with the BP Neural Network. We leveraged the distinct advantages of both algorithms - utilizing ACO's pheromone-based path optimization for feature selection and initial weight initialization, while employing BP's gradient descent method for fine-tuning neural network weights. The hybrid implementation demonstrates enhanced performance through ACO's global search capabilities combined with BP's local convergence precision. Our research results validate the potential and value of this combined approach, providing significant references and insights for further studies and applications in related fields. This innovative integration has achieved remarkable progress in solving complex problems and optimization tasks, utilizing code structures that implement pheromone update rules for path optimization and error backpropagation mechanisms for network training. The methodology opens up broad development possibilities for future research and practical applications in intelligent optimization systems.