The Optimal Routing of Cars in the Car Navigation System by Taking the Combination of Divide and Conquer Method and Ant Colony Algorithm into Consideration

Abstract

Abstract-In this paper we propose an optimal routing method for cars in car navigation system. The proposed method finds the paths with a combination of Divide and Conquer method and Ant Colony algorithm. In order to do this, the road network is divided to small areas. Then the learning operation is done in these small areas. Then different learnt paths are combined together to make the complete paths. This method causes balance and reduces the traffic in lanes of the pathes, because it not only consider lengths of the paths for learning operations, it also considers the other factor which is traffic condition of the lanes of the paths. Consequently this method reduces the average triptime of the cars in comparison with existing Ant Colony, Genetic and Dijkstra algorithms by reducing and balancing of road traffic. Therefore, some improvements in the average traveling time of vehicles are achieved. Also, this results in short and precise paths and learning stage becomes faster.

Improving the performance of TAntNet-2 by using multi forward scouts

“…The research in [7] presented a modified ACO algorithm for finding routes based on a cooperative pheromone among Ants. In [10] a method for vehicles navigation system has been proposed, this method depends on integration between Divide and Conquer method and Ant Colony algorithm. In [24] a city based parking routing system that depend on Ant based routing has been introduced.…”

“…One of the most promising approaches of swarm intelligence is the Ant routing methodology, this approach inspired from the behavior of real ants when foraging, this methodology capable of finding near optimal solutions at low computational cost. Ant routing algorithms have been studied in many researches [6][7][8][9][10][11][12][13][14][15]. This paper presents a modification for TAntNet algorithm; the new modification adopted a new methodology of lunching multi forward agents that make the algorithm able to early detect the good routes and avoid the bad effect of strayed forward agents.…”

Improve Traffic Management in the Vehicular Ad Hoc Networks by Combining Ant Colony Algorithm and Fuzzy System

“…Reference [9] have also proposed an optimal routing approach using a machine learning algorithm to reduce vehicles` travel time by combining the ant colony algorithm and path length based learning methods.…”

Ant colony optimisation for vehicle traffic systems: applications and challenges

“…Machine learning Yousefi and Zamani (2013) The optimal routing of cars Propose an optimal routing in the car navigation system by method to reduce vehicles travel taking the combination of divide time by combining divide and and conquer method and ant conquer, ACO and learning colony algorithm into approaches. consideration Jiang et al (2007) Solving the shortest path Propose a shortest path problem in vehicle navigation search method by modifying system by ant colony algorithm pheromone update rule and adding learning strategy into ACO.…”

“…it has been applied in the cases of travelling salesman problem (Lucic and Teodorovic, 2001, 2002, 2003a and transportation problem (Lucic and Teodorovic, 2003b;Teodorovi´c and Dell'Orco, 2005;Senge andWedde, 2012a, 2012b). Sur et al (2012), Suson (2010), Yousefi and Zamani (2013) and Tatomir et al (2009) proposed the use of a multi-agent system (MAS) with ant agents to solve multi-criteria shortest path problem and reduce vehicle congestion. By investigating ant behaviour, researchers have recognised that each ant randomly explores its surrounding to find food resources.…”