A strengthen learning and associative memory particle swarm optimization（SLAM-PSO）algorithm is presented

and the algorithm is applied to node localization in wireless sensor networks（WSNs）.With different ranging error based on RSSI model

comparing the localization error and localization variance generated by maximum likelihood estimation method and SLAM-PSO algorithm

and it proves that the SLAM-PSO algorithm is a fast convergence

high precision

good stability optimization algorithm

and it is suitable for application in WSNs node positioning.