Abstract: To solve the coverage optimization problem of wireless sensor networks (WSN), this paper proposes the dynamic parameter-controlled virtual force enhanced particle swarm optimization (DPVF-PSO) algorithm for region fence coverage. First, the network parameters and particle swarm parameters are initialized, and the initial positions and velocities of the particles are randomly generated. The fitness values of each particle are then calculated. During the iteration process, the PSO parameters are dynamically adjusted, virtual forces between nodes are computed, target fence regions are selected, and the shortest path is determined using D* algorithm. The particles’ velocities and positions are updated, and fitness evaluations are performed. Every few generations, a local search is conducted to further optimize the global best solution. In particular, within detection areas containing obstacles, DPVF-PSO uses a virtual force mechanism to guide nodes around obstacles, while also leveraging D* algorithm to plan the shortest path, optimize node movement trajectories, reduce transmission delays, and conserve energy, ensuring the effectiveness of network coverage and communication connectivity. Experimental results show that, in environments with obstacles, the coverage rate of DPVF-PSO exceeds that of the Chaos ABC, IIC-CS, IHPO, and HHO algorithms by 3.623%, 5.762%, 10.643%, and 4.385%, respectively. This makes it highly applicable to practical scenarios such as intelligent traffic management and environmental monitoring, demonstrating significant practical value.