<![CDATA[This research focused on modification ACO algorithm. The purpose of this research was to obtain better performance by modifying the algorithm of Ant Colony Optimization (ACO), particularly in terms of computational speed while maintaining the quality of the solution. Modifications made in this research is the calculation of the probability of the next city to be visit, the ant trail intensity calculations, and modification number of ants to follow the size of the problem. From the results of this research it can be concluded that by modifying the probability of the next city to be visit and the intensity of the ant trail can maintain the quality of the resulting solution with a percentage of 99.8% while the number of ants that used 35% of the size of the problem. In terms of memory usage, modification ACO algorithm is more efficient than the original ACO algorithm with an average of 7%. In addition, the time required to generate the shortest path on average three times faster than the original ACO.Keywords – ACO, ACO Modified, Shortest Path]]>
Copyrights © 2014
