This paper addresses the coverage problem in wireless sensor networks (WSNs) by proposing an improved grey wolf optimizer with multi-strategies (IGWO-MS). The authors aim to enhance the coverage rate and reduce deployment costs by optimizing the deployment of sensor nodes. The IGWO-MS algorithm incorporates several enhancements to the standard grey wolf optimizer (GWO), including: 1. **Sobol Sequence Initialization**: This method ensures a more uniform distribution of the initial population, improving the diversity and stability of the algorithm. 2. **Search Space Strategy**: This strategy increases the search range of the population, preventing premature convergence and improving search accuracy. 3. **Reverse Learning and Mirror Mapping**: These techniques enhance the diversity of the population and improve the algorithm's ability to escape local optima. 4. **Levy Flight**: This method adds disturbance to the algorithm, increasing the probability of jumping out of local optima. The coverage problem is transformed into a single-objective optimization problem by rasterizing the coverage area into multiple grids. The performance of IGWO-MS is compared with other algorithms (PSO, GWO, DGWO, and GWO-THW) in simulation experiments. The results show that IGWO-MS outperforms the other algorithms in terms of both optimal and average coverage rates, demonstrating its effectiveness in solving WSN coverage problems. The experimental data and visualizations further illustrate the superior performance of IGWO-MS in achieving uniform node distribution and reducing coverage redundancy.This paper addresses the coverage problem in wireless sensor networks (WSNs) by proposing an improved grey wolf optimizer with multi-strategies (IGWO-MS). The authors aim to enhance the coverage rate and reduce deployment costs by optimizing the deployment of sensor nodes. The IGWO-MS algorithm incorporates several enhancements to the standard grey wolf optimizer (GWO), including: 1. **Sobol Sequence Initialization**: This method ensures a more uniform distribution of the initial population, improving the diversity and stability of the algorithm. 2. **Search Space Strategy**: This strategy increases the search range of the population, preventing premature convergence and improving search accuracy. 3. **Reverse Learning and Mirror Mapping**: These techniques enhance the diversity of the population and improve the algorithm's ability to escape local optima. 4. **Levy Flight**: This method adds disturbance to the algorithm, increasing the probability of jumping out of local optima. The coverage problem is transformed into a single-objective optimization problem by rasterizing the coverage area into multiple grids. The performance of IGWO-MS is compared with other algorithms (PSO, GWO, DGWO, and GWO-THW) in simulation experiments. The results show that IGWO-MS outperforms the other algorithms in terms of both optimal and average coverage rates, demonstrating its effectiveness in solving WSN coverage problems. The experimental data and visualizations further illustrate the superior performance of IGWO-MS in achieving uniform node distribution and reducing coverage redundancy.