Neural Networks Optimized with Ant Colony Algorithm

Ant Colony Optimization (ACO) is a swarm intelligence algorithm that mimics the foraging behavior of ants in nature. In recent years, researchers have explored the integration of ACO with neural networks to optimize weight parameters and structural configurations. This hybrid approach significantly enhances training efficiency and generalization capabilities of neural networks.

In ACO-based neural network optimization, the objective function (typically implemented as ANT_object_func_ant in code) correlates with neural network performance metrics such as loss functions or accuracy rates. The algorithm's core mechanism simulates the pheromone trail selection behavior of ants in solution space, gradually converging toward optimal solutions. In practical implementation, each "ant" represents a candidate solution set of neural network parameters. The algorithm iteratively updates pheromone concentrations to guide the search direction, ultimately outputting the optimal parameter combination through multiple generations of colony evolution.

The main program (often structured as Ant_ant_new) orchestrates the complete ACO workflow, including colony initialization, fitness calculation, pheromone matrix updating, and optimal solution selection. This algorithm demonstrates particular effectiveness for high-dimensional parameter optimization problems, effectively circumventing local optima issues commonly encountered in traditional gradient descent methods. The implementation typically involves probability-based path selection mechanisms and evaporation rules for pheromone persistence control.

This integration provides novel approaches for neural network optimization, especially suitable for complex nonlinear modeling challenges. Future research directions include exploring its application potential in large-scale deep learning models and developing parallel computing implementations for accelerated optimization processes.