WSN coverage optimization based on improved grey wolf optimization algorithm
- Resource Type
- article
- Authors
- GAO Min; LIU Hairong; ZHU Yanfei
- Source
- Journal of Shanghai Normal University (Natural Sciences), Vol 52, Iss 2, Pp 256-263 (2023)
- Subject
- wireless sensor network(wsn)
network coverage
grey wolf optimization(gwo) algorithm
nonlinear convergence factor
differential evolution(de) algorithm
Science (General)
Q1-390
- Language
- English
Chinese
- ISSN
- 1000-5137
We consider the problem of low coverage of wireless sensor network(WSN) nodes caused by uneven distribution during random deployment. An improved gray wolf optimization(GWO) algorithm was proposed. The population was initialized by using Tent chaotic map to increase the diversity of the population. The improved nonlinear convergence factor was used to balance the global search ability and local search accuracy of the algorithm. Mutation and crossover of differential evolution(DE) algorithm were integrated into GWO algorithm to avoid the algorithm falling into local optimization and improve the convergence speed of the algorithm. The simulation results of the basic test function verify the effectiveness of the improved algorithm. The improved GWO was applied to the WSN coverage optimization problem, which can make the node distribution more uniform, improve the coverage and the network performance.