An enhanced gravitational search algorithm for node deployment in wireless sensor networks

被引：0
作者：
Guo Z. [1 ]
Peng J. [1 ]
Yin B. [1 ]
Wang S. [2 ]
Yue X. [1 ]
Liu X. [3 ]
机构：
[1] School of Science, Jiangxi University of Science and Technology, Ganzhou
[2] School of Information Engineering, Hebei GEO University, Shijiazhuang
[3] School of Architectural and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou
来源：
International Journal of Wireless and Mobile Computing | 2019年 / 17卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Global optimisation; Gravitational search algorithm; Node deployment; Wireless sensor networks;
D O I：
10.1504/ijwmc.2019.10022315
中图分类号：
学科分类号：
摘要：
Node deployment is a fundamental technique in wireless sensor networks, which can be converted into an optimisation issue. Gravitational Search Algorithm (GSA) is a popular optimisation method, which has exhibited promising performance for node deployment. However, the traditional GSA may show poor convergence when tackling some complicated node deployment issues. To enhance the search efficiency, an enhanced GSA (NDGSA) is introduced for node deployment issue in wireless sensor networks. In NDGSA, it creates new solutions according to a linear combination of the current solution and the value drawn from Gaussian distribution. In the experiments, NDGSA is compared with the traditional approaches on the node deployment issue. The comparisons validate the efficiency of NDGSA. Copyright © 2019 Inderscience Enterprises Ltd.
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页码：36 / 42
页数：6
相关论文
共 33 条
  • [1] Barani F., Mirhosseini M., Nezamabadi-Pour H., Application of binary quantum-inspired gravitational search algorithm in feature subset selection, Applied Intelligence, 47, 2, pp. 304-318, (2017)
  • [2] Beigvand S.D., Abdi H., La Scala M., Optimal operation of multicarrier energy systems using time varying acceleration coefficient gravitational search algorithm, Energy, 114, pp. 253-265, (2016)
  • [3] Bohat V.K., Arya K.V., An effective gbest-guided gravitational search algorithm for real-parameter optimization and its application in training of feedforward neural networks, Knowledge-Based Systems, 143, pp. 192-207, (2018)
  • [4] Bostani H., Sheikhan M., Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems, Soft Computing, 21, 9, pp. 2307-2324, (2017)
  • [5] Cuevas E., Diaz P., Avalos O., Zaldivar D., Perez-Cisneros M., Nonlinear system identification based on ANFIS-Hammerstein model using gravitational search algorithm, Applied Intelligence, 48, 1, pp. 182-203, (2018)
  • [6] Darzi S., Kiong T.S., Islam M.T., Soleymanpour H.R., Kibria S., A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming, Applied Soft Computing, 47, pp. 103-118, (2016)
  • [7] Feng T., Zhong Y., Liu X., Yu L., Application in deformation prediction based on LSSVR model optimized by AABC, Journal of Jiangxi University of Science and Technology, 39, 3, pp. 35-39, (2018)
  • [8] Garcia S., Fernandez A., Luengo J., Herrera F., Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power, Information Sciences, 180, 10, pp. 2044-2064, (2010)
  • [9] Guo Z., Wang S., Yin B., Liu S., Liu X., A hybrid harmony search algorithm for node localisation in wireless sensor networks, International Journal of Wireless and Mobile Computing, 14, 4, pp. 369-377, (2018)
  • [10] Guo Z., Yang H., Liu S., Liu X., Gravitational search algorithm with Gaussian mutation strategy, International Journal of Wireless and Mobile Computing, 12, 2, pp. 191-197, (2017)
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