WIRELESS SENSOR NETWORKS BASED ON IMPROVED DUNG BEETLE ALGORITHM
Volume 7, Issue 3, Pp 65-75, 2025
DOI: https://doi.org/10.61784/jcsee3058
Author(s)
QiYuan Shen1*, YunLong Xia2
Affiliation(s)
1College of Electronics and Information (Micro-Nano Technology College), Qingdao University, Qingdao 266071, Shandong, China.
2College of Electrical Engineering and Information Technology, Lanzhou University of Technology, Lanzhou 730050, Gansu, China.
Corresponding Author
QiYuan Shen
ABSTRACT
To address the limitations of traditional Dung Beetle Optimization (DBO) in the wireless sensor network coverage problem, specifically like low convergence speed and susceptibility to local optima, this paper proposes an optimization scheme based on the Improved DBO algorithm (IDBO). The algorithm combines three key strategies: first, a Logistic chaos initialization strategy is used to generate a more optimal initial solution; second, a Levy flight strategy is introduced to enhance the global search capability; and finally, the search process is further optimized by using a dynamic nonlinear convergence factor to adaptively adjust the search step size. With the above improvements, the algorithm is significantly improved in global search performance. Experiments on the CEC2005 benchmark suite show that IDBO outperforms similar algorithms and approaches the global optimum. Finally, at last, the proposed IDBO algorithm is applied to the wireless sensor network coverage optimization problem for simulation experiments. The simulation results show that the improved IDBO algorithm improves the coverage of the network nodes by 4.9% compared to the basic DBO algorithm and enhances the overall performance of the network with good practicality, stability and robustness.
KEYWORDS
Dung beetle optimization; Wireless sensor network coverage; Logistic chaotic initialization; Levy flight strategy; Dynamic nonlinear convergence factor
CITE THIS PAPER
QiYuan Shen, YunLong Xia. Wireless sensor networks based on improved dung beetle algorithm. Journal of Computer Science and Electrical Engineering. 2025, 7(3): 65-75. DOI: https://doi.org/10.61784/jcsee3058.
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