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PROJECT


Maze Solver Using the Ant Colony Optimization Algorithm

This project uses a novel nature-inspired Ant Colony Optimization algorithm (ACO) to find an optimal path from a maze of different walls. Learn how to plot the maze, and the final optimized path returned from the ACO on the maze.

Maze Solver Using the Ant Colony Optimization Algorithm

You will learn to:

Implement a metaheuristic using Python.

Solve maze problems in a programmatic way.

Skills

Data Plotting

Combinatorial Optimization

Probabilistic Metaheuristics

Nature Inspired Algorithms

Prerequisites

Intermediate knowledge of Python

Basic understanding of the metaheuristics

Basic understanding of the Jupyter Notebook

Basic understanding of Ant Colony Optimisation (ACO) Algorithm

Technologies

NumPy

Python

Matplotlib

Project Description

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.

Maze with different paths

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.

Project Tasks

1

Algorithm Implementation

Task 0: Get Started

Task 1: Import Modules

Task 2: Load the Maze Data into a List

Task 3: Create a Cell Class

Task 4: Create Data Cells

Task 5: Add Connections between Cells

Task 6: Find Entry and Exit Points

Task 7: Create an Ant Class

Task 8: Create Utility Methods

Task 9: Create a Method to Get a Path

Task 10: Get Options

Task 11: Choose the Next Cell

Task 12: Remove Cycles

Task 13: Evaporate and Deposit the Pheromone

2

Finding the Path

Task 14: Set the Parameters of the ACO

Task 15: Run the Algorithm

Task 16: Get the Optimal Path

3

Plot the Path

Task 17: Plot the Actual Path on Maze

Congratulations