Advertisement

Improved Ant Colony Optimization Algorithm for Optimized Nodes Deployment of HAP-Based Marine Monitoring Sensor Networks

  • Jianli DuanEmail author
  • Yuxiang Liu
  • Bin LinEmail author
  • Yuan Jiang
  • Fen Hou
  • Wantong Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)

Abstract

Territorial ocean safety and ocean development make it important to establish a large-scale, long-term, and low-energy integrated ocean monitoring sensor network (OMSN). In this paper, we introduce the high attitude platform-based ocean monitoring sensor network (HAP-OMSN) architecture and the basic ant colony optimization (ACO) algorithm first. And then, we propose an improved ant colony optimization algorithm for the node deployment of the HAP-OMSN architecture. Finally, we solve the multi-types node deployment (MTND) problems in HAP-OMSN using this algorithm. The final experiment results indicate that the improved ACO algorithm has good efficiency to find optimal solution.

Keywords

Ant colony optimization (ACO) Ocean monitoring sensor network (OMSN) The high attitude platform-based OMSN (HAP-OMSN) 

Notes

Acknowledgements

This study is sponsored by National Science Foundation of China (NSFC) No. 61371091 and No. 61301228, Liaoning Provincial Natural Science Foundation of China No.2014025001, and Program for Liaoning Excellent Talents in University (LNET) No. LJQ2013054.

References

  1. 1.
    Liu, Y.: Research on Optimized Nodes Deployment of HAP—Based Marine Monitoring Sensor Networks. Dalian Maritime University, Dalian (2018)Google Scholar
  2. 2.
    Gan, R., Guo, Q., Chang, H., et al.: Improved ant colony optimization for the traveling salesman problems. J. Syst. Eng. Electron. 21(2), 329–333 (2010)CrossRefGoogle Scholar
  3. 3.
    Stutzle, T., Hoos, H.: The MAX-MIN ant system and local search for the traveling salesman problem. In: IEEE 4th International Conference on Evolutionary Computation, pp. 308–313 (1997)Google Scholar
  4. 4.
    Meng, X., Huang, T., Chen, S.: Improved ant colony optimization algorithm based on pheromone updating and evaporation factor adjusting. J. Chengdu Univers. Nat. Sci. Ed. 34(1), 48–51 (2015)Google Scholar
  5. 5.
    Li, L., Yu, H.: Improved ant colony algorithm in complex environments on the robot path planning. J. Chin. Comput. Syst. 38(9), 2067–2071 (2017)Google Scholar
  6. 6.
    Xiang, U., Liang, Z., Wei, Z., et al.: Dynamic path planning in RoboCup rescue simulation competition. In: The 27th Chinese Control and Decision Conference, pp. 4341–4344 (2015)Google Scholar
  7. 7.
    Qu, H., Huang, L., Ke, X.: Research of improved ant colony based robot path planning under dynamic environment. J. Univ. Electron. Sci. Technol. China. 44(2), 260–265 (2015)Google Scholar
  8. 8.
    Liu, J., Yan, Q., Ma, Y., et al.: Global path planning based on improved ant colony optimization algorithm for geometry. J. Northeast. Univ. (Nat. Sci.). 36(7), 923–928 (2015)zbMATHGoogle Scholar
  9. 9.
    Duan, H., Wang, D., Zhu, J., et al.: Development on ant colony algorithm theory and its application. Control Decis. 19(12), 1321–1320 (2004)Google Scholar
  10. 10.
    Fu, Y.: The Improvement and Application of Ant Colony Algorithm. Shanghai Maritime University, Shanghai (2006)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Dalian Maritime UniversityDalianChina
  2. 2.Qingdao University of TechnologyQingdaoChina
  3. 3.University of Chinese Academy of ScienceBeijingChina
  4. 4.University of MacauMacauChina

Personalised recommendations