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A General and Effective Network Failure Ant Colony Algorithm Based on Network Fault Location Methods

  • Ruan Ling
  • Liu Changhua
  • Wang Yuling
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)

Abstract

With the development and evolution of network technology, the normal operation of network equipment to meet the most basic needs of the needs of business users. Meanwhile, the key is the normal operation of network services. Therefore, this paper proposes to quickly find a network failure ant colony algorithm, which uses equipment pheromone concentration determines the strength of the network devices failure probability, according to the pheromone concentration construction business failed path.

Keywords

Ant colony algorithm Network failure Failed path Pheromone concentration 

Notes

Acknowledgment

This work was supported by The National Science Foundation for Young Scientists of China (No. 61201452) and the Graduate Innovation Fund of Wuhan Polytechnic University (Grant No. 2013cx014).

References

  1. 1.
    Maniezzo, V., Carbonaro, A: Ant colony optimization: an over view. In: Ribeiro, C. (ed.) Essays and Surveys in Metaheuristics, pp. 21–44. Kluwer (2001)Google Scholar
  2. 2.
    Guojiang, X.: A Power Grid Fault Diagnosis Method Based on Computational Intelligence. Huazhong University of Science and Technology, Wuhan (2014)Google Scholar
  3. 3.
    Xian, X., Zhang, Y., Cheng, H.: Ant colony algorithm in WSN QoS routing optimization. J. Comput. Simul. 395–398 (2015)Google Scholar
  4. 4.
    Zhou, P.: The general identification and solution method of computer network fault analysis. J. Sci. Technol. Commun. 121–135 (2015)Google Scholar
  5. 5.
    Zhou, J.: Comparison and simulation of two typical congestion control algorithms for TCP protocol. J. Qiqihar Univ. (Nat. Sci. Edn.) 27–29 (2016)Google Scholar
  6. 6.
    Lei, W.: Design and Implementation of Network Protocol Analyzer for East Coal Exploration Bureau. College of Computer Science, Jilin University, Jilin (2014)Google Scholar
  7. 7.
    He, R., Xiong, N., Yang, L.T., Park, J.H.: Using multi-modal semantic association rules to fuse keywords and visual features automatically for web image retrieval. Information Fusion 12(3), 223–230 (2011)CrossRefGoogle Scholar
  8. 8.
    Yang, Y., Xiong, N., Chong, N.Y., Défago, X.: A decentralized and adaptive flocking algorithm for autonomous mobile robots. In: GPC Workshops 2008 (Grid and Pervasive Computing Workshops) (2008)Google Scholar
  9. 9.
    Tan, L., Zhu, Z., Ge, F., Xiong, N.: Utility maximization resource allocation in wireless networks: methods and algorithms. IEEE Trans. Syst. Man Cybern. Syst. 45(7), 1018–1034 (2015)CrossRefGoogle Scholar
  10. 10.
    Wan, Z., Xiong, N., Ghani, N., Vasilakos, A.V., Zhou, L.: Adaptive unequal protection for wireless video transmission over IEEE 802.11e networks. Multimed. Tools Appl. 72(1), 541–571 (2014)CrossRefGoogle Scholar
  11. 11.
    Xiong, N., Han, W., Vandenberg, A.: Green cloud computing schemes based on networks: a survey. IET Commun. 6(18), 3294–3300 (2012)CrossRefGoogle Scholar
  12. 12.
    Xiong, N., Vasilakos, A.V., Wu, J., Yang, Y.R., Rindos, A., Zhou, Y., Song, W.Z., et al.: A self-tuning failure detection scheme for cloud computing service. In: IEEE 26th International Conference About Parallel & Distributed Processing Symposium (IPDPS) (2012)Google Scholar
  13. 13.
    Guo, W., Xiong, N., Vasilakos, A.V., Chen, G., Yu, C.: Distributed k–connected fault–tolerant topology control algorithms with PSO in future autonomic sensor systems. Int. J. Sens. Netw. 12(1), 53–62 (2012)CrossRefGoogle Scholar
  14. 14.
    Wang, X., Li, Q., Xiong, N., Pan, Y.: Ant colony optimization-based location-aware routing for wireless sensor networks. In: International Conference on Wireless Algorithms, Systems, and Applications (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.College of Mathematics and Computer ScienceWuhan Polytechnic UniversityWuhanChina

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