An implementation of Ant Colony Optimization(ACO) in Matlab is used for optimal travel route in specific instances of the Symmetric Travelling Salesman Problem and compare the results with that of Genetic Algorithm(GA) by running them on the same symmetric TSP models. Several papers on similar topics and experiments conducted are reviewed. Statistical analysis methods are then used to attempt to find the model-specific ideal values for pheromone and heuristic exponential weight, as well as the pheromone evaporation rate, parameters not shared by GA, while keeping parameters shared by both algorithms the same. Even so, results conclude that GA performs better on all metrics as compared to ACO – faster with less computational time per iteration proportional to number of agents, while providing better solutions at all ranges. While ACO provides better solutions with fewer iterations, it takes more time per iteration as compared to GA, which ends up providing better solutions in the same timespan with more iterations. The possibility is mentioned that these results may be skewed as ACO has more parameters and require more fine-tuning as compared to GA per model, which may not have been done perfectly. There is also the possibility that the ACO implementation in MATLAB was coded in such a way that it added unnecessary computational time. ACO is also an umbrella term for many variants unlike GA, such as the initial Ant System which was later improved into Ant Colony System, so the implementation might be of an older ACO variant.
Volume 12 | 07-Special Issue
Pages: 2629-2635
DOI: 10.5373/JARDCS/V12SP7/20202399