Improved Ant Colony Optimization Algorithm for Optimized Nodes Deployment of HAP-Based Marine Monitoring Sensor Networks
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.
KeywordsAnt colony optimization (ACO) Ocean monitoring sensor network (OMSN) The high attitude platform-based OMSN (HAP-OMSN)
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.
- 1.Liu, Y.: Research on Optimized Nodes Deployment of HAP—Based Marine Monitoring Sensor Networks. Dalian Maritime University, Dalian (2018)Google Scholar
- 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.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.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.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.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
- 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.Fu, Y.: The Improvement and Application of Ant Colony Algorithm. Shanghai Maritime University, Shanghai (2006)Google Scholar