Solving the 76-City TSP Problem Using Ant Colony Algorithm in MATLAB

This article presents a MATLAB implementation of the ant colony optimization algorithm to solve the 76-city Traveling Salesman Problem (TSP). The algorithm utilizes key components including pheromone matrix initialization, probabilistic path selection using roulette wheel selection, and dynamic pheromone updating with evaporation mechanisms. Through iterative optimization, the solution converges toward globally optimal routes. This approach demonstrates advanced techniques for handling large-scale TSP instances, featuring parameters like alpha (pheromone influence), beta (heuristic information weight), and evaporation rate for balance between exploration and exploitation. The implementation includes visualization of convergence curves and optimal route mapping, making it particularly valuable for researchers and practitioners interested in metaheuristic optimization algorithms. By studying this implementation, readers can gain deep insights into ant colony system applications for combinatorial optimization problems and explore further algorithmic enhancements for improved performance. This technical demonstration aims to inspire advanced understanding of both ant colony algorithms and TSP problem-solving methodologies.