Wireless Sensor Network Coverage Optimization Based on the Novel Enhanced Hunter–Prey Optimization Algorithm

Optimizing the coverage of wireless sensor nodes is a critical issue in wireless sensor networks (WSNs). Current coverage optimization techniques often suffer from local optimization problems, resulting in poor optimization effects, ample coverage blind areas, and uneven distribution. Therefore, based on the hunter-prey optimization (HPO) algorithm, we propose a novel enhanced HPO (NEHPO) algorithm to address its drawbacks of low search precision and easy fall into local optima, achieving enhanced coverage performance. First, the initial population is deployed using Sobol sequence sampling to increase diversity. Second, we propose a novel nonlinear convergence factor to balance global exploration and local exploitation. Finally, the improved algorithm employs Lévy flight and vertical-horizontal crossover strategies to mitigate the late-stage local optimum problem of the HPO algorithm. The particle swarm optimization (PSO), sine-cosine algorithm (SCA), sparrow search algorithm (SSA), HPO, and NEHPO are numerically tested by a benchmark function, and the experimental results show that the convergence performance and global search ability of the NEHPO algorithm are better than those of the other four algorithms. For the WSN coverage problem, this article tests the above five algorithms using four cases involving variations in node count, node radius, monitoring area size, and deployment in different obstacle regions. Compared to the other four algorithms, simulation results indicate that the NEHPO algorithm achieves the most uniform distribution of network nodes, the highest network coverage and optimization rates, the best convergence performance, and more robust adaptability for optimizing node deployment in obstacle environments.