Node Localization Method for Wireless Sensor Networks Based on Hybrid Optimization of Differential Evolution and Particle Swarm Algorithm

Abstract

Regarding the node localization problems for wireless sensor network, a hybrid optimization method was pro- posed accordingly on differential evolution(DE) algorithm and particle swarm optimization(PSO) algorithm. Firstly, the position and velocity of the initial population were randomly generated by PSO, and the fitness function was constructed according to the mean square error of estimated and measured distance between the unknown nodes and their adjacent an- chor node. Secondly, the mutation and selection operation of DE algorithm were executed to find out the optimum posi- tion of the population. Lastly, the current velocities and positions of all particles of the population were updated, and the crossover operation and selection operation of DE algorithm were executed to update the current global optimum position of the whole population. Population global optimum solution of iterative search algorithm is the position coordinate of the unknown node. Simulation results indicate that the proposed localization method has smaller average localization error and higher localization accuracy than that of DE algorithm and PSO algorithm in the same environment.