MATLAB Implementation of Ant Colony Optimization for 31-City Vehicle Routing Problem (VRP)

In this document, I would like to share a MATLAB source code implementation for solving the Vehicle Routing Problem (VRP) with 31 cities using Ant Colony Optimization (ACO). The ACO algorithm is a heuristic approach inspired by ant foraging behavior, simulating how ants communicate and cooperate to find optimal paths through pheromone trails. This MATLAB implementation includes key components such as distance matrix calculation, pheromone initialization, probabilistic path selection using roulette wheel selection, and pheromone update rules (evaporation and reinforcement). The code structure features main functions for colony initialization, iteration control, and solution visualization, with configurable parameters for ant population size, evaporation rate, and convergence criteria. This practical implementation is particularly valuable for research and practical projects requiring optimization of large-scale inter-city routing problems, providing insights into metaheuristic algorithm application for combinatorial optimization challenges.