Particle Swarm Optimization Algorithm Based on Dynamic Acceleration Factor in Wireless Sensor Network

The coverage problem is one basic problem in the wireless sensor networks (WSN). In one limited region, how to reasonably arrange the sensor nodes to achieve the best coverage is the key to improve the performance of the whole networks. So this paper proposes an improved particle swarm optimization algorithm based on dynamic acceleration factor (PSO DAC). It adopts decreasing inertia weight coefficients and introduces dynamic acceleration coefficients. The experimental results show that the algorithm has improved the coverage ratio by 34.6% than that of the standard particle swarm algorithm (SPSO), which is 29.3% higher than the particle swarm algorithm based on the decreasing inertia weight coefficient (LDWPSO). It is proved that the PSO-DAC algorithm can effectively improve the convergence speed and improve the coverage rate of nodes, so as to improve the coverage effect of the whole network and prolong the network lifetime.