Ant Colony Optimization (ACO) for Traveling Salesman Problem (TSP) with MATLAB Implementation

This program implements the Ant Colony Optimization (ACO) algorithm in MATLAB to solve the Traveling Salesman Problem (TSP) using the Oliver30 dataset, which contains distance information between 30 cities. ACO is a heuristic algorithm inspired by the foraging behavior of ants. The algorithm simulates how ants deposit pheromones while searching for optimal paths. In this implementation, multiple ants perform parallel searches - each ant selects its next city based on pheromone concentration and distance to neighboring cities. After visiting all cities, each ant returns to its starting point. Key implementation details include: - Pheromone matrix initialization and updating mechanisms - Probabilistic city selection using both pheromone levels and distance heuristics - Iterative optimization process with global pheromone updating - Path length calculation and optimal solution tracking The program features: - Parallel ant colony simulation with configurable parameters - Dynamic pheromone evaporation and reinforcement - Best-path preservation across iterations - Modular design allowing easy dataset replacement To adapt this solution for other TSP datasets, simply replace the distance matrix while maintaining the core ACO algorithm structure. The implementation efficiently converges to near-optimal solutions through iterative pheromone-based path optimization.