Strategies DACS3 Increasing its Performances

Ant Colony System (ACS) is the most popular algorithm used to find a shortest path solution in Traveling Salesman Problem (TSP). Several ACS versions have been proposed which aim to achieve an optimum solution by adjusting pheromone levels. Embedding Malaysian House Red Ant behavior into ACS known as Dynamic Ant Colony Systems with Three Level Updates (DACS3) has increased the ability to reach shortest path. However, embedding such behavior has reduced the performance of the algorithm. Therefore, this paper presents the improvement of DACS3 by applying several strategies focuses on the improvement of the local techniques such as dynamic candidate list, smoothing and elitist ant. The performance of DACS3 is measured based on shortest distance and time taken to reach the solution against original DACS3 and ACS algorithms on TSP datasets ranging from 14 to 159 cities. The result shows that appyling several strategics into DACS3 has increased its ability to reach the shortest distance for most of the data and performs better for most datasets accept for Bier127, however the differences is very small.