MATLAB Source Code for Ant Colony Optimization Algorithm

Ant Colony Optimization (ACO) is a heuristic search algorithm inspired by the foraging behavior of ants, commonly used to solve combinatorial optimization problems. The algorithm simulates the process of ants releasing pheromones on paths to progressively find optimal solutions.

Implementing ACO in MATLAB typically requires multiple source code files working together, covering core algorithm logic, path selection, pheromone update mechanisms, and visualization capabilities. These files generally handle the following modules:

Parameter Initialization: Sets key parameters such as ant population size, pheromone decay coefficient, and heuristic factors through configuration variables or input functions. Path Construction: Each ant selects paths probabilistically using roulette wheel selection or tournament selection, combining pheromone concentration and heuristic information through probability calculation functions. Pheromone Update: Implements global or local pheromone update strategies using matrix operations to reinforce the attractiveness of optimal paths. Fitness Calculation: Evaluates path quality through objective functions, such as calculating total path length in Traveling Salesman Problems (TSP) using distance matrices. Iterative Optimization: Employs while/for loops with convergence checks to gradually approach near-optimal solutions through multiple iterations. Visualization Output: Uses MATLAB plotting functions (plot, surf, etc.) to display path evolution, pheromone distribution, or convergence curves.

This modular design enhances code extensibility and maintainability, making it suitable for solving path planning, task scheduling, and similar optimization problems. For beginners, it's recommended to start with the core logic module, gradually understanding pheromone mechanisms and probability-based selection strategies through code tracing and parameter adjustment.