An improved fuzzy ant colony system for route selection based on real time traffic condition - Erick Alfons Lisangan; Sean Coonery Sumarta (IAES International Journal of Artificial Intelligence (IJ-AI), 2021)
With city conditions that often have congestion, a driver need to find a route of the many possible routes that may occur from origin to destination by considering several factors. The weaknesses of the ant algorithm is the dependency of required parameter values and must be set manually. This paper will make improvements to the fuzzy ant colony system (FACS) with minimize parameter dependency by using fuzzy logic to determine the probability of the next node visited by ants. There are four criteria or input variables for fuzzy inference system, that is “Pheromone Intensity”, “Distance”, “Vehicle Intensity”, and “Average Speed”. The routes generated by improved FACS (I-FACS) are more varied than the FACS algorithm. The best condition obtained by I-FACS was at point O/D= 112/34 where at 05:00, I-FACS was able to obtain the route with the best length compared to ACS and FACS. The result of the comparison of the route distance obtained shows that I-FACS is able to produce a better route by 4.16% than FACS by looking at the average distance difference on ACS. This method is expected to be a reference for the development of smart transportation in support of smart mobility, which is one of the components in the smart city concept.