Optimization of WSNs Coverage for Power Monitoring Based on Multi-strategy Sand Cat Swarm Optimization Algorithm

With the rapid development of electric power technology, the application of wireless sensor networks (WSNs) in the electric power field has received widespread attention. The WSNs coverage optimization problem in the power field is one of the key factors to improve the power transmission efficiency and resource utilization. However, traditional optimization algorithms have some limitations in dealing with the deployment problem of large-scale sensor nodes. Therefore, this thesis proposes a multi-strategy sand cat swarm optimization algorithm (MSSCSO) to solve the WSNs coverage problem. First, this paper proposes an improved Tent chaos mapping to improve the search range and enhance the local search capability of the algorithm. Secondly, the reverse learning strategy is used to increase the population diversity and global search capability, which improves the convergence speed and optimization accuracy of the algorithm. Finally, the global search capability of the algorithm and the adaptability and robustness of the algorithm are enhanced by the adaptive strategy. In order to evaluate the performance of the proposed method, this paper conducts comparative experiments with Genetic Algorithm (GA), Firefly Optimization Algorithm (FA), Whale Optimization Algorithm (WOA) and Gray Wolf Optimization Algorithm (GWO), respectively. The experimental results show that MSSCSO's WSNs coverage optimization method can effectively improve the network coverage in power scenarios by 10.54%, 20.04%, 9.81%, and 6.43% over GA, FA, WOA, and GWO, respectively. Meanwhile, the method has strong adaptability and stability to the uncertainty and dynamic changes in the power environment.