Abstract: Dynamic deployment of sensor nodes is random, which can not guarantee the coverage quality of specific target areas. Intelligent optimization algorithm is introduced to effectively improve the quality of dynamic deployment of sensor nodes. However, the general intelligent optimization algorithm has some shortcomings such as “premature” in dynamic deployment. In order to further improve the quality of dynamic deployment of nodes, this paper studies the coverage problem of nodes, combines the advantages of particle swarm optimization and differential evolution, uses particle swarm optimization in the early stage to give full play to the characteristics of particle swarm optimization which is good at global search, and uses differential evolution algorithm in the later stage to give full play to the characteristics of differential evolution which is good at local search, so as to take the advantages of both and overcome the shortcomings of both. The algorithm has better search ability. The simulation results show that the new algorithm has better search ability and better network coverage than the improved inertia weight particle swarm optimization algorithm, the virtual force particle swarm optimization algorithm and the basic differential evolution algorithm.

Key words: wireless sensor networks, particle swarm optimization, differential evolution algorithms, node coverage