基于二阶段差分演化的WSN节点定位优化

由于非测距的WSN节点定位算法DV-Hop定位精度不高，引入智能优化算法后有效提高了定位精度，但迭代次数过大，节点能耗相对过高，而在较少信标节点和较短的通讯信半径条件下，传统智能优化算法难以生效。针对这种情况，提出了基于二阶段的差分演化定位优化算法。仿真实验设计在100m×100m正方形的区域内，随机分布100个无线传感器节点，首先用DV-Hop算法进行第一阶段粗略定位，然后在第二阶段用差化演化算法对定位进行优化，为了对比各种算法在低能耗（很少迭代次数）下的表现，优化过程只迭代了10代，最后得到节点坐标。实验结果表明，算法能获得更好的定位精度和具有更好的稳定性。该算法在极少迭代次数的条件下，在信标节点稀疏和通信半径较短的特殊情况下，获得满意的定位精度和更好的稳定性。

WSN Node Location Optimization Based on Second-stage Differential Evolution

Because the DV-Hop localization accuracy of distance-independent WSN node localization algorithm is not high, intelligent optimization algorithm is introduced to improve the localization accuracy effectively, but the number of iterations is too large and the energy consumption of nodes is relatively high. When there are fewer anchor nodes and shorter communication radius, the traditional intelligent optimization algorithm is difficult to take effect. In view of this situation, a two-stage differential evolution location optimization algorithm is proposed.The simulation experiment is designed to randomly distribute 100 wireless sensor nodes in a square area of 100m x 100m, DV-Hop algorithm is used to locate roughly in the first stage, then differential evolution algorithm is used to optimize the location in the second stage, in order to compare the performance of various algorithms under low energy consumption (few iterations), the optimization process only iterates for 10 generations,and finally the coordinates of nodes are obtained. The experimental results show that the algorithm can achieve better positioning accuracy and stability. Under the condition of few iteration algebras, the algorithm achieves satisfactory positioning accuracy and better stability under the special circumstances of sparse anchor nodes and short communication radius.