An Adaptive Hybrid Differential Grey Wolf Optimization algorithm for WSN coverage.

Abstract

To address the issues of large blind spots and uneven distribution in traditional Wireless Sensor Network (WSN) node deployment, we propose an Adaptive Hybrid Differential Grey Wolf Optimization (AHDGWO) algorithm for solving the WSN coverage problem in 2D area. Firstly, an adaptive exponential convergence factor is designed, allowing each individual to adjust global exploration and local exploitation adaptively. Secondly, by integrating the concept of differential mutation, an hourglass-shaped random search area is established. This not only prevents blind search but also bolsters the algorithm’s global exploration capabilities. The AHDGWO algorithm’s performance is compared against the standard GWO, its advanced variants and several recent advanced evolutionary algorithms, using the CEC2022 test suite. The results demonstrate that the AHDGWO algorithm achieves better accuracy and convergence speed. Finally, the performance of AHDGWO is evaluated in 2D WSN scenario through simulations. The experimental results show that the coverage rate optimized by the AHDGWO algorithm surpasses that of the other seven comparison algorithms, indicating its practicality and scalability for WSN coverage problems.