Ant Colony Optimization for Shortest Path Finding

This is a MATLAB-based implementation of the Ant Colony Optimization (ACO) algorithm designed for shortest path detection in networks. The program utilizes probabilistic path selection inspired by ant foraging behavior, where pheromone trails are dynamically updated to converge toward optimal routes. The included package contains predefined network node coordinates, enabling accurate spatial representation and distance calculations between nodes. Key algorithmic components include pheromone initialization, probability-based node transition rules, and iterative pheromone evaporation/reinforcement mechanisms. Through this implementation, users can observe how ACO balances exploration and exploitation to avoid local minima while achieving path optimization. The code structure demonstrates practical applications of swarm intelligence in solving combinatorial optimization problems, with modular functions for path visualization and performance metrics analysis. This program serves as an educational tool for understanding ACO dynamics and can be adapted for real-world routing scenarios.