基于模拟退火和樽海鞘群优化的DV-Hop定位算法

DV-Hop localization algorithm based on simulated annealing and optimization of salp swarm

针对无线传感器网络(WSNs)中传统DV-Hop算法定位误差较大等问题,提出基于模拟退火和樽海鞘群优化的DV-Hop定位算法。该算法分别引入RSSI和修正因子来量化最小跳数以及校正平均跳距,在未知节点估计过程中,采用改进的樽海鞘群优化算法代替最小二乘法,并且与模拟退火算法相结合,缓解了樽海鞘群优化算法在寻优过程中容易陷入局部最优的缺点。仿真结果表明:改进后的DV-Hop算法相比于传统DV-Hop定位算法以及其他智能优化算法,定位精度得到明显改善。

Aiming at the problem of large positioning error of traditional DV-Hop algorithm in wireless sensor networks(WSNs),a DV-Hop positioning algorithm based on simulated annealing and optimization of salp swarm is proposed.The algorithm introduces RSSI and correction factor to quantify the minimum hops and correct the average hop distance respectively.In the process of unknown node estimation,the improved salp swarm optimization algorithm is used to replace the least square method,and combined with simulated annealing algorithm,which alleviates the disadvantage that the salp swarm optimum algorithm is easy to fall into local optimum in the optimization process.The simulation results show that the positioning precision of the improved DV-Hop algorithm is significantly improved,compared with the traditional DV-Hop positioning algorithm and other intelligent optimization algorithms.