Classic Ant Colony Optimization Algorithm MATLAB Source Code

This is a well-structured MATLAB implementation of the classic Ant Colony Optimization (ACO) algorithm. The code includes key components such as pheromone initialization, probabilistic path selection using roulette wheel selection, and pheromone update mechanisms with evaporation rates. While C++ implementation offers performance advantages, MATLAB's matrix operations and plotting capabilities make algorithm prototyping and visualization significantly more straightforward. The ACO algorithm is a nature-inspired heuristic optimization method that simulates ant foraging behavior to solve complex problems. This implementation demonstrates core algorithmic components including: - Pheromone matrix initialization and update procedures - Probability calculation for path selection using visibility and pheromone concentrations - Iterative optimization process with convergence monitoring This MATLAB source code serves as an excellent starting point for understanding ACO fundamentals, with clear variable naming and commented sections explaining the algorithm's workflow. Potential applications include route optimization, image processing, machine learning tasks, and combinatorial optimization problems. The code structure allows easy modification of parameters like ant population size, evaporation rate, and iteration counts for different problem domains.