Reducing iteration using candidate list

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 a newly developed algorithm called dynamic ant colony system 3 level updates (DACS3). The principle of cooperation and the behavior of a single ant finding path has been the backbone in this algorithmic development. 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, a long searching time required by DACS3 to find the best solution is a problem factor that needs to be addressed. Thus, adding a suitable strategy like candidate list concept would helps DACS3 improve itspsila performance. 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 several benchmark data to show improvement of DACS3 algorithm.