Research on the Shortest Path of Two Places in Urban Based on Improved Ant Colony Algorithm

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10228)

Abstract

Based on the GIS electronic map and traffic control information database, a shortest path algorithm based on GIS technology is proposed, the A and B geographic information of the monitoring points are extracted, and the shortest path algorithm is used to solve the shortest path between A and B. Using the improved ant colony algorithm to calculate the shortest distance from the start node to the target node. In view of the phenomenon of ant colony algorithm convergence speed is slow and easy to fall into premature defects, and the effective measures for improvement was put forward, and take the simplifying road network as an example, a simulation of the algorithm was conducted. The satisfactory results of the simulation verify the effectiveness of the algorithm.

Keywords

Ant colony algorithm Simulation Path Convergence speed 

Notes

Acknowledgment

This research work was supported by the Nature Science Foundation of China, and the project name is “Research on the theory and method of manufacturability evaluation in cloud manufacturing environment”, no. 51405030; the Youth Science Foundation of Jilin Province, no. 20160520069JH.

References

  1. 1.
    He, M., Liang, W., Chen, G., Chen, Q.: Topology of mobile underwater wireless sensor networks. Control Decis. 28(12), 1761–1770 (2013)Google Scholar
  2. 2.
    Cai, W., Zhao, H., Wang, J., Lin, C.: A unifying network topological model of the energy internet macro-scope structure. Proc. CSEE 35(14), 3503–3510 (2015)Google Scholar
  3. 3.
    Wang, S., Xing, J., Zhang, Y., Bai, B.: Ellipse-based shortest path algorithm for typical urban road networks. Syst. Eng. Theory Prac. 31(6), 1158–1164 (2011)Google Scholar
  4. 4.
    Bi, F.: Application of the Optimal Path Planning in the Supervision of Land Enforcement System. China University of Mining and Technology (2014)Google Scholar
  5. 5.
    Lu, F., Lu, D., Cui, W.: Time shortest path algorithm for restricted searching area in transportation networks. J. Image Graph. 4(10), 849–853 (1999)Google Scholar
  6. 6.
    Manish, M., Vimal, B.: A low-complexity hybrid algorithm based on particle swarm and ant colony optimization for large-MIMO detection. Exp. Syst. Appl. 50, 66–74 (2016)CrossRefGoogle Scholar
  7. 7.
    Li, Q., Zhang, C., Chen, P., Yin, Y.: Improved ant colony optimization algorithm based on particle swarm optimization. Control Decis. 28(6), 873–883 (2013)MATHGoogle Scholar
  8. 8.
    Song, D., Zhang, J.: Batch scheduling problem of hybrid flow shop based on ant colony algorithm. Comput. Integr. Manuf. Syst. 19(7), 1640–1647 (2013)Google Scholar
  9. 9.
    Zhang, J., Zhang, P., Liu, G.: Two-stage ant colony algorithm based job shop scheduling with unrelated parallel machines. J. Mech. Eng. 49(6), 136–144 (2013)CrossRefGoogle Scholar
  10. 10.
    Enxiu, S., Minmin, C., Jun, L., Yumei, H.: Research on method of global path-planning for mobile robot based on ant-colony algorithm. Trans. Chin. Soc. Agric. Mach. 45(6), 53–57 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yanjuan Hu
    • 1
    • 2
  • Luquan Ren
    • 1
  • Hongwei Zhao
    • 1
  • Yao Wang
    • 3
  1. 1.College of Biological and Agricultural EngineeringJilin UniversityChangchunChina
  2. 2.Mechatronic EngineeringChangchun University of TechnologyChangchunChina
  3. 3.College of Mechanical EngineeringBeihua UniversityJilin CityChina

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