Vehicle Routing Problem (VRP) with Ant Colony Optimization Algorithm

This code is designed to solve the Vehicle Routing Problem (VRP), where we aim to find optimal routes that minimize the total travel distance for all vehicles, thereby improving transportation efficiency. We implement the Ant Colony Optimization (ACO) algorithm, which is inspired by the natural foraging behavior of ants and simulates how ants follow pheromone trails to locate food sources. The algorithm initializes multiple artificial ants that construct solutions probabilistically based on pheromone intensity and heuristic information (e.g., distance between nodes). Key components include pheromone update rules (evaporation and reinforcement) and a fitness function to evaluate route quality. Through iterative optimization, the algorithm converges to near-optimal vehicle routes, enhancing delivery efficiency while reducing time and operational costs. The implementation includes parameters for colony size, iteration count, and pheromone decay rate to balance exploration and exploitation during search.