Abstract
Ant colony algorithm is easy to fall in local best and its convergent speed is slow in solving multiple QoS constrained unicast routing problems. Therefore, an ant colony algorithm based on monopoly and competition is proposed in this paper to solve the problems. In the choice of nodes, improves pheromone competition, avoids monopoly of pheromone prematurely, stimulates ants to attempt the paths which have less pheromone and improves the global search ability of ants. Stagnation behavior is judged by the monopoly extent of the pheromone on the excellent path. Moreover, the catastrophic is embedded in the global pheromone update operation. According to simulations, its global search is strong and it can range out of local best and it is fast convergence to the global optimum. The improved algorithm is feasible and effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, Z., Corwcroft, J.: Quality-of-service routing for supporting multimedia applications. IEEE J. Sel. Areas Commun. 14(7), 1228–1234 (1996)
Li, H.J., Jing, Y.Y., Liu, H.J.: Research on secure QoS routing algorithm for distributed fiber Bragg grating sensor networks. Laser J. 38(6), 89–92 (2017)
Sun, X.X., Wang, X.W., Huang, M.: Adaptive harmony PSO based trusted QoS routing scheme. J. Syst. Simul. 28(3), 741–748 (2016)
Liu, H.Y., Sun, F.C.: Satellite networks QoS routing algorithm based on an orthogonal polynomials neural network. J. Tsinghua Univ. (Sci. Technol.) 53(4), 556–561(2013)
Colorni, A., Dorigo, M., Maniezzo, V., et al.: Distributed optimization by ant colonies. In: Varela, F., Bourgine, P. (eds.) Proceedings of the ECAL 1991, European Conference of Artificial Life, pp. 134–144. Elsevier, Paris (1991)
Xu, K., Lu, H., Cheng, B., Huang, Y.: Ant colony optimization algorithm based on improved pheromones double updating and local optimization for solving TSP. J. Comput. Appl. 37(6), 1686–1691 (2017)
Zhang, H.G., Gong, X.: A Generalized ant colony algorithm for job-shop scheduling problem. J. Harbin Univ. Sci. Technol. 22(1), 91–95, 102 (2017)
You, X.M., Liu, S., Lv, J.Q.: Ant colony algorithm based on dynamic search strategy and its application on path planning of robot. Control Decis. 32(3), 552–556 (2017)
Gao, L.C.: QoS routing algorithm base on Q-learning and improved ant colony in mobile ad hoc networks. J. Jilin Univ. (Sci. Ed.). 53(3), 483–488 (2015)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. System Man Cybern. Part B 26(1), 29–41 (1996)
Acknowledgment
This work is supported by the Application Research Program of 2016 the Guangxi province of China young and middle-aged teachers basic ability promotion project (No. KY2016YB133), the Research Program of 2014 Guagnxi University for Nationalities of China (No. 2014MDYB029), the Key project of science and technology research in Guangxi education (No. 2013ZD021), the innovation team project of xiangsihu youth scholars of Guangxi University For Nationalities, and the Research Program of 2014 Guagnxi University for Nationalities of China (No. 2014MDYB028).
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
Li, Y., Huang, Y., Xuan, S., Qu, L. (2018). Application of Ant Colony Algorithm Based on Monopoly and Competition Idea in QoS Routing. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-02698-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02697-4
Online ISBN: 978-3-030-02698-1
eBook Packages: Computer ScienceComputer Science (R0)