Ant Colony Optimization Class

This repository contains a Python implementation of an Ant Colony Optimization (ACO) class and an example application solving the Traveling Salesman Problem (TSP).

Contents

1. ant_colony_optimization.py: Contains the AntColonyOptimization class, a flexible implementation of the ACO algorithm.
2. aco_tsp_example.py: Demonstrates how to use the AntColonyOptimization class to solve a Traveling Salesman Problem.

How to Use

Requirements

• Python 3.6 or higher
• No additional libraries required

Running the TSP Example

1. Clone this repository:

git clone https://github.com/yourusername/aco-tsp-solver.git
cd aco-tsp-solver

2. Run the TSP example:

python aco_tsp_example.py

This will execute the ACO algorithm to find an optimal solution for the TSP defined in the script.

Using the AntColonyOptimization Class

To use the AntColonyOptimization class for your own problems:

1. Import the class:

from ant_colony_optimization import AntColonyOptimization

2. Define your distance function. It should take two city indices as input and return the distance between them.

3. Initialize the ACO:

aco = AntColonyOptimization(
    num_ants=10,
    num_cities=your_num_cities,
    distance_func=your_distance_function,
    alpha=1.0,
    beta=2.0,
    evaporation_rate=0.5,
    q=100,
    initial_pheromone=1.0
)

4. Run the algorithm:

best_solution = aco.run(iterations=100)

5. Interpret the result based on your problem domain.

Customization

• Modify the alpha, beta, evaporation_rate, and q parameters to fine-tune the algorithm's performance.
• Adjust the num_ants and number of iterations based on your problem's complexity and computational resources.
• For problems other than TSP, you may need to modify the construct_solution and update_pheromone methods in the AntColonyOptimization class.

Contributing

Contributions to improve the algorithm or add new features are welcome. Please feel free to submit pull requests or open issues for any bugs or enhancements.

License

This project is open-source and available under the MIT License.