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Using an Ant Colony Optimisation algorithm to solve the Traveling Salesman Problem for the ECM3412 Nature-Inspired Computation coursework exercise. Achieved 90%

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To run the code, run aco.py.

Python 3.11.1 was used for the development of this program.

Changeable Variables:

  • filename: The name of the TSP xml file to read.
  • number_of_ants: The number of ants to use in the algorithm.
  • alpha: The importance of the pheromone trail.
  • beta: The importance of the heuristic information.
  • rho: The pheromone evaporation rate.
  • q: The amount of pheromone deposited by an ant on a trail after completing a tour.
  • number_of_trials: The number of trials to run the program and get the best result from those.

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Using an Ant Colony Optimisation algorithm to solve the Traveling Salesman Problem for the ECM3412 Nature-Inspired Computation coursework exercise. Achieved 90%

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