Hybrid Particle Swarm Optimization-based Modeling of Wireless Sensor Network Coverage Optimization

To address the problem of insufficient coverage WSN and poor network in obstacle environments, study proposes an improved particle swarm optimization (PSO) combined with a hybrid grey wolf algorithm. The speed position PSO particle's search for superiority are enhanced through guiding nature superior (GWO), thus convergence precision improved. Based on this, applies to wireless sensor networks (WSO) model uses comparison test effectiveness According results, node PSO, genetic algorithm (GA), data envelopment analysis (DEA), GWO, (GWPSO) reach 85.97%, 87.24%, 88.76%, 89.31%, 91.05% respectively trapezoidal environment. And research-designed GWPSO reaches highest value its kind. This shows that has performance control deployment compared similar algorithms. design provides new path optimizing coverage.