基于佳点集的蝙蝠定位算法在WSN中应用

A Positioning Algorithm Based on Bat Algorithm and Good-Point Setsin the Application of WSN

针对无线传感器网络(WSN)节点的定位误差较大的的问题,提出一种新的基于佳点集的蝙蝠定位算法。在改进的算法中,采用基于佳点集的方法对蝙蝠种群个体进行初始化优化,有效提高种群多样性,避免算法过早陷入局部最优;引入部落机制及自适应更新方式,可有效避免局部最优解的吸引,加快收敛速度;通过重构部落利用pareto分级有效避免个别优秀个体被淘汰,增强了泛化能力,提高算法精度。通过MATLAB模拟仿真平台仿真实验表明,改进后的算法具有较好的收敛性和良好的寻优性能,降低测距误差对定位的影响,提高节点的定位精度。算法系统实现条件简单、精度高,具有较高的实际应用价值。

In order to solve the problem that node localization error in wireless sensor network( WSN) is large,this paper proposes a new bat positioning algorithm based on good point set. In the improved algorithm,the bat population individual is optimized by the good point set method,which can effectively improve the population diversity and prevent the algorithm from falling into the local optimum; The method by introducing tribal mechanism and adaptive updating can effectively avoid attracting the local optimal solution and expedite the convergence speed; Reconstructing the tribe by pareto classification can avoid eliminating the isolated outstanding individuals,enhance the generalization ability and improve the algorithm precision. By the simulation experiments on MATLAB,the results show that the improved algorithm has good convergence and searching performance,also reduces the influence of ranging error on positioning,and improves the nodes positioning accuracy. The algorithm is simple in implementation,high in precision and high in practical value.