Aiming at the problem of poor coverage quality and low connectivity caused by random node deployment in wireless sensor network(WSN)

a multi-objective WSN node deployment optimization model(MNDOM)with coverage and connectivity as performance indexes is developed.In view of the premature convergence problem of the multi-objective grey wolf optimization(MOGWO)algorithm

an improved MOGWO(IMOGWO)algorithm is developed to solve the WSN node deployment optimization problem with multiple performance indexes.Therein

the Tent chaotic map is used to enhance the diversity of individuals in the initial population

and a new three-stage nonlinear convergence factor and an adaptive position update strategy are taken to dynamically balance the capabilities of global exploration and local exploitation.Comparative experimental results show that the WSN model’s design is reasonable

and the proposed algorithm not only has obvious advantages in the optimization performance for different benchmark test problems

but also can provide a variety of optional WSN node deployment schemes.