Maze Solver Using the Ant Colony Optimization Algorithm

Ant Colony Optimization (ACO) is a novel metaheuristic to solve combinatorial optimization problems. This algorithm mimics the behavior of ants in real life to get a good approximate maze solution. ACO employs artificial ants to build solutions by adding components based on heuristic information about the problem and pheromone trails that reflect the acquired search experience.

Maze solving is a problem in Artificial Intelligence (AI). The ultimate goal is to find a successful and optimized path in the maze by avoiding all the walls in the maze. In the first section of this project, we’ll complete the ACO implementation. After implementing the algorithm, we will load and visualize the maze.

We’ll use the following maze for this project. It has different paths from the entry point leading to the exit point of the maze.

We’ll find an optimal path using ACO. At the end of this project, we’ll plot the maze, and the path returned from the algorithm.