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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
He, M., Liang, W., Chen, G., Chen, Q.: Topology of mobile underwater wireless sensor networks. Control Decis. 28(12), 1761–1770 (2013)
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)
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)
Bi, F.: Application of the Optimal Path Planning in the Supervision of Land Enforcement System. China University of Mining and Technology (2014)
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)
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)
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)
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)
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)
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)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Hu, Y., Ren, L., Zhao, H., Wang, Y. (2017). Research on the Shortest Path of Two Places in Urban Based on Improved Ant Colony Algorithm. In: Zhang, L., Ren, L., Kordon, F. (eds) Challenges and Opportunity with Big Data. Monterey Workshop 2016. Lecture Notes in Computer Science(), vol 10228. Springer, Cham. https://doi.org/10.1007/978-3-319-61994-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-61994-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61993-4
Online ISBN: 978-3-319-61994-1
eBook Packages: Computer ScienceComputer Science (R0)