Solving the shortest path problem in vehicle navigation system by ant colony algorithm

A shortest path search method based on ant colony algorithm is proposed. The method contracts the search space appropriately, obtaining, in a short time, a path that is as close as possible to the path obtained by the Dijkstra method (the optimum path). To improve the performance, we modify the pheromone update rule and introduce a learning strategy into the ant colony algorithm. The method obtains a solution to a problem given within a specified time, such as path search in a vehicle navigation system. The effectiveness of the method is described through use of simulations.

[1]  Frank Harary,et al.  Graph Theory , 2016 .

[2]  Yee Leung,et al.  A genetic algorithm for the multiple destination routing problems , 1998, IEEE Trans. Evol. Comput..

[3]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[4]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[5]  J. A. Bondy,et al.  Graph Theory with Applications , 1978 .

[6]  Chang Wook Ahn,et al.  A genetic algorithm for shortest path routing problem and the sizing of populations , 2002, IEEE Trans. Evol. Comput..

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[9]  Da Yuan,et al.  Solving a shortest path problem by ant algorithm , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).