Neural Network Optimized with Ant Colony Algorithm

This article presents a neural network optimization framework utilizing the Ant Colony Algorithm. The ant colony optimization method serves as a metaheuristic approach for enhancing neural network performance. Our implementation includes ANT_object_func_ant, which defines the neural network-based objective function that guides the ant movement through solution space, and Ant_ant_new, the main program that orchestrates the ant colony optimization process. The algorithm employs pheromone trail updates and probabilistic path selection to iteratively improve neural network parameters. Through this ant colony-based optimization approach, we achieve significant performance improvements in neural network training and obtain superior results compared to conventional optimization methods. The system implements key swarm intelligence mechanisms including positive feedback through pheromone deposition, evaporation mechanisms to avoid local optima, and heuristic information integration for efficient exploration of neural network parameter space.