MATLAB Source Code for Ant Colony Optimization Algorithm

This repository provides MATLAB source code implementations related to Ant Colony Optimization (ACO) algorithms, designed to solve various optimization problems including path planning, maximum value optimization, and the Traveling Salesman Problem (TSP). Ant Colony Optimization is a heuristic algorithm inspired by the foraging behavior of ant colonies, where artificial ants deposit pheromones to communicate and find optimal paths through iterative search processes. The code implementations feature key ACO components such as pheromone initialization, probability-based path selection using roulette wheel selection, and pheromone update mechanisms (evaporation and reinforcement). The TSP solver includes distance matrix computation and tour construction algorithms, while path planning modules incorporate obstacle avoidance and shortest-path detection. Maximum value optimization implementations utilize fitness function evaluation and global best solution tracking. These ready-to-use MATLAB scripts enable rapid problem-solving with configurable parameters for colony size, iteration counts, and evaporation rates. The code structure includes modular functions for easy customization and extension to specific optimization scenarios, providing efficient and accurate solutions for complex real-world engineering problems.