Ant Colony Optimization Algorithm Paper with MATLAB Source Code for Beginners

This paper and accompanying MATLAB source code introduce the application domains and methodologies of Ant Colony Optimization (ACO) algorithm, providing fully executable simulation code that enables beginners to quickly grasp its implementation process. The ACO algorithm demonstrates wide applicability in practical scenarios such as path planning, data clustering, and image segmentation. The paper provides in-depth exploration of these applications through MATLAB implementations featuring key functions including pheromone initialization, probability-based path selection, and dynamic pheromone update mechanisms. Each code module contains detailed annotations explaining parameter configuration, iteration control, and convergence criteria. Practical examples illustrate the complete implementation workflow from problem modeling to optimization results visualization, demonstrating how to adjust parameters like evaporation rate and heuristic factors for different scenarios. The provided code implements core ACO components including ant movement simulation, fitness evaluation, and global/local pheromone update strategies. Through this resource, readers will gain comprehensive understanding of ACO's theoretical foundations and practical implementation techniques, enabling immediate application to real-world optimization problems. The modular code structure allows easy adaptation to specific use cases while maintaining algorithmic integrity.