Continuous Assessment for ECM3412 - Nature-Inspired Computation, set by Prof. Ayah Helal (Year 3, Semester 1). Implements the ant colony optimisation algorithm to address the travelling salesperson problem for two given networks. Please see results/results.pdf for insights on results and written answers to questions.
This work received a final mark of 70/100.
Please see specification.pdf for specification.
Project was developed in Python 3. Install prerequisites with:
pip install -r requirements.txtaco.py and elitist-aco.py require ant.py and load_data.py to run.
The program is executed from aco.py. To run the program, please use
python aco.py
To run elitist-aco.py, please use
python elitist-aco.py
Details on modifying parameters/variables to reflect experiments can be found in the Python source code. This is done at the call of the main method, right at the bottom of the script.
Results shown in results/results.pdf and the associated text files, which can be found in results/BurmaResults/ and results/BrazilResults/. Execution durations are also stored in the text files, and images are included in the directories.