Hybrid Algorithm Combining Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for Solving Traveling Salesman Problem (TSP)

MATLAB code implementation of a hybrid algorithm combining Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for solving the Traveling Salesman Problem (TSP). Ant Colony Optimization is a heuristic optimization algorithm that simulates ant foraging behavior, where artificial ants communicate through pheromone trails to find optimal paths. Particle Swarm Optimization mimics bird flock foraging behavior, optimizing solutions through velocity and position updates of particles in the search space. The hybrid algorithm leverages the complementary strengths of both methods: ACO's strong exploitation capabilities for path construction and PSO's efficient global exploration. In the MATLAB implementation, key functions include pheromone matrix initialization for ACO, velocity updates using PSO equations, and a hybrid transition rule combining both algorithms' decision mechanisms. The code typically involves setting parameters like colony size, evaporation rate, and inertia weight, with iterative optimization cycles where PSO guides global search while ACO refines local path selection. This integrated approach achieves improved convergence speed and solution quality for TSP instances compared to standalone algorithms.