Improved Particle Swarm Localization Algorithm Based on Evolutionary Mechanism

Abstract

Aiming at the problem of low localization accuracy and poor robustness when conventional localization algorithms are used for the localization of WSN nodes, an improved particle swarm localization algorithm based on an evolutionary mechanism is proposed. To reduce the ranging error in a complex environment, the anchor box algorithm is improved by using ranging information, and the search area is reduced by the improved algorithm to determine the possible region where unknown nodes exist. Then the objective function is established to transform the localization problem into a bounded optimization problem. The conventional particle swarm optimization algorithm is improved and genetic operators such as crossover and mutation are introduced to improve the search accuracy and global optimization ability of the algorithm. The simulation results show that, compared with the conventional positioning algorithm, the proposed algorithm has smaller positioning errors and stronger robustness.