The Coverage Improvement of the Wireless Sensor Network Based on the Parameters Optimized Honey Badger Algorithm

In this study, we presented a Parameters-Optimized HBA (POHBA) to enhance the optimization performance of the HBA. The POHBA improves the network coverage rate of the Wireless Sensor Network without increasing the algorithm complexity by optimizing the parameters
$C$
and
$\beta $
of the initial HBA. We defined the optimization rate (r_opt) for the first time to compare the optimization effect significantly of different algorithms or the same algorithm in different experimental scenarios. In our experiments, the optimization performance of POHBA is superior to the initial HBA. Meanwhile, we compared the performance of the POHBA, Grey Wolf Optimization (GWO) algorithm, and Particle Swarm Optimization (PSO) algorithm in four scenarios with identical experimental settings. In all simulation experiments, the most uniform network node distribution, the highest network coverage and optimization rate, and the best convergence performance were all obtained by POHBA. Especially in the large-scale network, POHBA kept excellent optimization performance. To identify the insight causes of why POHBA’s performance is superior to GWO and PSO, we applied the dimension-wise diversity measurement for the first time, by which we found the best consistency in the ratios of exploration and exploitation and the highest proportion of the exploitation throughout the iterations obtained by POHBA, and that is the insight reason why an algorithm can get a superior optimization performance in our study.