Reinforcement Learning-Driven Hunter-Prey Algorithm Applied to 3D Underwater Sensor Network Coverage Optimization

As one of the key application scenarios of wireless sensor networks, the coverage optimization of underwater wireless sensor networks (UWSNs) requires special consideration of three-dimensional spatial characteristics, which distinctly differs from traditional terrestrial environment coverage issues. To address the problems of low coverage and uneven distribution in UWSNs within a three-dimensional space, we propose a Reinforcement Learning-driven Hunter-Prey Optimization (RL-HPO) algorithm. Firstly, a nonlinear convergence factor is designed to regulate the exploration and exploitation phases, achieving an effective balance between these two stages. Secondly, by incorporating the concept of Q-learning, the algorithm can adaptively select the optimal action strategy at different stages, thereby enhancing the effectiveness of actions executed in each phase. Lastly, the Nelder-Mead simplex strategy is introduced to perturb poorly performing individuals within the population, fully exploiting their search potential and preventing the algorithm from getting trapped in local optima. The performance of the RL-HPO algorithm in three-dimensional WSN environments, both with and without obstacles, was evaluated through simulation experiments. Comparisons were made with PSO, HPO, SSA, ALGWO, and SWOA. The results demonstrate that RL-HPO significantly outperforms other algorithms in key metrics such as coverage rate, moving distance, and network connectivity. In obstacle-free scenarios, RL-HPO achieved the highest coverage rate of 96.5%, while in scenarios with obstacles, the coverage rate reached 93.3%, representing improvements of 12.58%, 13.41%, 4.15%, 8%, and 13.95% over the other algorithms, respectively.