Information technologies WSN coverage hierarchical optimization method based on the improved MOEA/D

Coverage is a fundamental problem in wireless sensor network (WSN), which is defined as the measurement of the quality of surveillance of sensing function. The concerns of coverage optimization are the maximize coverage rate and the minimize energy consumption. In this paper, we proposed the multi-objective evolutionary algorithm based on decomposition with particle swarm optimization (MOEA/D-PSO). Through preserving the high-quality individuals in present generation, improving the local optimization solution set in evolutionary search direction and the search progress, eventually propose to make up for the shortcomings of multi-objective evolutionary algorithm based on decomposition (MOEA/D). Compared with MOEA/D and non-dominated sorting genetic algorithm-II (NSGA-II), the results show that the MOEA/D-PSO is closer to Pareto optimal surface, the performs better in uniformity and diversity of solution set distribution. WSN has a broader coverage and consumes less energy.