Improved Dynamic Ant Colony System (DACS) on symmetric Traveling Salesman Problem (TSP)

Ants are a fascinating creature that demonstrates a capability of finding food and bring it back to their nest. Their ability as a colony to find paths or routes to the food sources has inspired the development of an algorithm namely ant colony system (ACS). The principle of cooperation has been the backbone in these algorithmic developments. However, observing the behavior of a single ant can provide an added value to the principle. Ants communicate to each other through a chemical substance called pheromone. Manipulating and empowering this substance is the trivial factor in finding the best solution. However, without considering the experiences of individuals would contribute a complete waste of available knowledge. Having the concepts of a single ant trying to reconstruct or reconnect the paths that was previously laid by its colony when a certain obstacle placed on its normal paths has added another level of pheromone updates. Thus, this new level of pheromone updates which manipulating and empowering the searching experiences of individual ants can improve the current ACS algorithm. Traveling salesman problem (TSP) was used as a case study to show the capability of the algorithm in order to find the best solution in terms of the shortest distance. At the end of this paper, we presented an experimental result on a benchmark data to show how it could improve the fundamental of ACS algorithm.