Coverage Optimization of Wireless Sensor Networks Based on Improved Particle Swarm Algorithm
In order to improve the coverage of Wireless Sensor Network(WSN),a Modified Particle Swarm Optimization(MPSO)algorithm based on mutual learning ability and dynamic learning factors is proposed.First,the Latin Hypercube Sampling(LHS)sequence is introduced to initialize the population to increase the diversity of the population and lay the foundation for the subsequent optimization.Secondly,a mutual learning method is introduced,in which target particles are randomly selected to enhance their own learning ability and improve local optimization performance.Finally,a dynamic learning factor strategy is used to accelerate the convergence speed and enhance the global optimization ability by changing the learning ability of the particles.The simulation results show that compared with the basic PSO algorithm and other algorithms,the MPSO algorithm can consume less resources to achieve better optimization results,effectively solve the problems of network coverage blind area and coverage redundancy,and improve the network coverage.
万方数据
维普
