Research of In-Car Navigation Based on Improved Ant Colony Algorithm

Abstract:

Determining how to select path efficiently in complex transportation networks was one of the main problems in-car navigation systems. For the drawbacks of slow convergence and easy to fall into local optimal solution of basic ant colony algorithm in solving the optimal path problem, a method of improving the expect-heuristic function is proposed in this paper, which enhances search direction and improves the convergence rate. Meanwhile, with the introduction of a new strategy to update the pheromone on ant colony system, the contradiction that convergence speed brings stagnation is balanced. The results show that the improved ant colony algorithm is easier to get the optimal solution compared with basic ant colony algorithm, and the convergence speed is faster, having a good navigation effect.

• Periodical: Applied Mechanics and Materials (Volumes 536-537)
• Pages: 461-465
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.536-537.461
• Online since: April 2014
• Authors: Fang Ding, Su Zhuo Wu*
• Keywords: Ant Colony Algorithm, Expect-Heuristic Function, In-Car Navigation, Optimal Path, Pheromone

References

[1] Xiaohang Cao: Modern Surveying and Mapping, Vol. 26 (2006), pp.14-17. (in Chinese).

[2] Jing Zhao: Global Positioning System, 2003, 3. (in Chinese).

[3] Feng Lu, Chenghu Zhou, Qin Wan: Geo-spatial Information Science, Vol. 3 (2000), pp.36-42. (in Chinese).

[4] Haibin Duan: Principle and Application of ant colony algorithm . Beijing: Science Press, 2005. (in Chinese).

[5] Zhaojun Zhang, Zuren Feng, Zhuqing Chen: Control and Decision, Vol. 9(2002), pp.1325-1330. (in Chinese).

[6] Michael J B K，Jean-Bernard B: Laurent K. Nature, Vol. 406(2000), pp.992-995.