Dual-Population Ant Colony Optimization for Solving the Shortest Path Problem across 75 Chinese Cities

The dual-population ant colony algorithm is an enhanced version of the classic ant colony optimization (ACO) method, specifically designed to solve the Traveling Salesman Problem (TSP) more efficiently. This algorithm employs two independent ant colonies for parallel search operations and utilizes a pheromone interaction mechanism to improve global optimization capabilities, effectively preventing premature convergence.

When solving the shortest path problem for 75 Chinese cities, the algorithm initializes two distinct populations that generate different initial solutions. Each ant colony selects paths probabilistically and updates local pheromone trails, while periodically exchanging optimal solutions to enhance search diversity. Compared to traditional single-population ACO, this dual-population strategy enables more comprehensive exploration of the solution space and increases the probability of finding the optimal path.

The MATLAB implementation provides a complete operational entry point through the main.m file, incorporating data loading, parameter configuration, iterative optimization, and result visualization functions. Users can run the code directly without modifications, making it suitable for path optimization research or educational demonstrations. The algorithm achieves an excellent balance between convergence speed and solution accuracy, serving as a valuable reference case in the field of intelligent optimization algorithms. Key implementation features include parallel population management, pheromone matrix synchronization, and adaptive parameter tuning mechanisms that contribute to the algorithm's robust performance.