The estimation error of the least square method in traditional Distance Vector-Hop (DV-Hop) algorithm is too large and the Particle Swarm Optimization (PSO) algorithm easily traps into local optimum. In order to overcome the problems, a fusion algorithm of improved particle swarm algorithm and DV-Hop algorithm was presented. First of all, PSO algorithm was improved from aspects of particle velocity, inertia weight, learning strategy and variation, which enhanced the ability of algorithm to jump out of local optimum and increased the search speed of the algorithm in later iterative stage. The node localization result was optimized by using the improved PSO algorithm in the third stage of the DV-Hop algorithm. The simulation results show that compared with the traditional DV-Hop algorithm,the improved DV-Hop based on chaotic PSO algorithm, and the DV-Hop algorithm based on improved PSO, the proposed algorithm has high positioning accuracy, good stability.

Concerning the problem that the least square method in the third stage of DV-Hop algorithm has low positioning accuracy, a localization algorithm was proposed which is the fusion of hybrid bat-quasi-Newton algorithm and DV-Hop algorithm. First of all, the Bat Algorithm (BA) was improved from two aspects: firstly, the random vector β was adjusted adaptively according to bats' fitness so that the pulse frequency had the adaptive ability. Secondly, bats were guided to move by the average position of all the best individuals before the current iteration so that the speed had variable performance; Then in the third stage of DV-Hop algorithm the improved bat algorithm was used to estimate node location and then quasi-Newton algorithm was used to continue searching for the node location from the estimated location as the initial searching point. The simulation results show that, compared with the traditional DV-Hop algorithm and the improved algorithm of DV-Hop based on bat algorithm(BADV-Hop), positioning precision of the proposed algorithm increases about 16.5% and 5.18%, and the algorithm has better stability, it is suitable for high positioning precision and stability situation.