Abstract
Public bicycle scheduling problem (PBSP) is a kind of problem that how to design a reasonable transportation route in order to reduce cost or improve user satisfaction under certain constraints. PBSP can be regarded as a specific combinatorial optimization problem that ant colony system (ACS) can solve. However, the performance of conventional ACS is sensitive to its parameters. If the parameters are not properly set, ACS may have the disadvantage of being easy to fall into local optimum, resulting in poor accuracy and poor robustness. This paper proposes an adaptive ACS (AACS) to efficiently solve PBSP. Instead of fixed parameters in ACS, each ant is configured with own different parameters automatically to construct solutions in AACS. In each generation, AACS regards the parameters in the well-performed ants as good parameters and spreads these parameters among the ant colony via selection, crossover, and mutation operators like in genetic algorithm (GA). This way, the key parameters of ACS can be evolved into a more suitable set to solve PBSP. We applied AACS to solve PBSP and compared AACS with conventional ACS and greedy algorithm. The results show that AACS will improve the accuracy of the solution and achieve better robustness.
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
Labadi, K., Benarbia, T., Barbot, J.P., Hamaci, S., Omari, A.: Stochastic petri net modeling, simulation and analysis of public bicycle sharing systems. IEEE Trans. Autom. Sci. Eng. 12(4), 1380–1395 (2015)
Wang, X., Choi, T.M., Liu, H., Yue, X.: Novel ant colony optimization methods for simplifying solution construction in vehicle routing problems. IEEE Trans. Intell. Transp. Syst. 17(11), 3132–3141 (2016)
Rabbani, M., Tahaei, Z., Farrokhi-Asl, H., Saravi, N.A.: Using meta-heuristic algorithms and hybrid of them to solve multi compartment vehicle routing problem. In: 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1022–1026. IEEE, Singapore (2017)
Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)
Zhang, Z., Zou, K.: Simple ant colony algorithm for combinatorial optimization problems. In: 36th Chinese Control Conference (CCC), pp. 9835–9840. IEEE, Dalian (2017)
Zhan, Z.H., Zhang, J., Li, Y., et al.: An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem. IEEE Trans. Intell. Transp. Syst. 11(2), 399–412 (2010)
Liu, X.F., Zhan, Z.H., Deng, J.D., Li, Y., Gu, T.L., Zhang, J.: An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans. Evol. Comput. 22(1), 113–128 (2018)
Chen, Z.G., Zhan, Z.H., Gong, Y.J., et al.: Multiobjective cloud workflow scheduling: a multiple populations ant colony system approach. IEEE Trans. Cybern. (2018). https://doi.org/10.1109/tcyb.2018.2832640
Mouhcine, E., Khalifa, M., Mohamed, Y.: Route optimization for school bus scheduling problem based on a distributed ant colony system algorithm. In: Intelligent Systems and Computer Vision (ISCV), pp. 1–8. IEEE, Fez (2017)
Liu, Z.P., Li, K.P., Zhu, X.H.: Optimal dispatch between stations for public bicycle based on ant colony algorithm. J. Transp. Inf. Saf. 30(4), 71–74 (2012)
Zhang, J.G., Wu, T., Jiang, Y.S.: Study on scheduling algorithm for public bicycle system based on ant colony algorithm. J. Xihua Univ. Nat. Sci. 33(3), 70–76 (2014)
Yu, W.J., Hu, X.M., Zhang, J., Huang, R.Z.: Self-adaptive ant colony system for the traveling salesman problem. In: 2009 IEEE International Conference on Systems, Man and Cybernetics, pp. 1399–1404. IEEE, San Antonio (2009)
Zecchin, A.C., Simpson, A.R., Maier, H.R., Nixon, J.B.: Parametric study for an ant algorithm applied to water distribution system optimization. IEEE Trans. Evol. Comput. 9(2), 175–191 (2005)
Jangra, R., Kait, R.: Analysis and comparison among ant system; ant colony system and max-min ant system with different parameters setting. In: 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT), pp. 1–4. IEEE, Ghaziabad (2017)
Gong, Y.J., Xu, R.T., Zhang, J., Liu, O.: A clustering-based adaptive parameter control method for continuous ant colony optimization. In: 2009 IEEE International Conference on Systems, Man and Cybernetics, pp. 1827–1832. IEEE, San Antonio (2009)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 26(1), 29–41 (1996)
Acknowledgments
This work was partially supported by the Outstanding Youth Science Foundation with No. 61822602, the National Natural Science Foundations of China (NSFC) with No. 61772207 and 61332002, the Natural Science Foundations of Guangdong Province for Distinguished Young Scholars with No. 2014A030306038, the Project for Pearl River New Star in Science and Technology with No. 201506010047, the GDUPS (2016), and the Fundamental Research Funds for the Central Universities.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Liang, D., Zhan, ZH., Zhang, J. (2018). An Adaptive Ant Colony System for Public Bicycle Scheduling Problem. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-04179-3_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04178-6
Online ISBN: 978-3-030-04179-3
eBook Packages: Computer ScienceComputer Science (R0)