基于加强学习与联想记忆粒子群优化算法的节点定位

摘要

提出了加强学习与联想记忆的粒子群优化算法,并将该算法应用到无线传感器网络的节点定位中.在RSSI模型测距产生的不同误差情况下,分别比较极大似然估计法和加强学习与联想记忆的粒子群优化算法产生的定位误差、定位方差,证明了加强学习与联想记忆的粒子群优化算法是一种收敛快、精度高、稳定性好的优化算法,适合应用在无线传感器网络节点定位中.%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.