基于改进粒子群算法的精准农业无线传感器定位研究

Research on Precise Agriculture Wireless Sensor Location Based on Improved Particle Swarm Optimization Algorithm

为了提高精准农业无线传感器定位的精度,提出改进粒子群算法。首先建立精准农业无线传感器定位过程;接着对粒子群算法的惯性权重进行非线性优化,使得算法前期变化缓慢,后期变化较快,利于算法跳出局部而求得全局最优解;然后对粒子群规模采取收缩扩张控制,其判别结合粒子的聚集度、多样性函数,算法前期的收缩扩张系数值在较大的位置,后期应减慢速度以加强算法的局部搜索能力;最后建立定位误差与粒子适应度函数关系。实验仿真显示本文算法收敛性能较好,相比其他算法能有效地抑制测距误差对定位的影响,提高节点的定位精度。

In order to improve the positioning accuracy of precision agriculture wireless sensor,an improved particle swarm optimization(PSO)algorithm was proposed.Firstly,the positioning process of precision agriculture wireless sensor is established.Then,the inertia weight of particle swarm optimization is nonlinearly optimized,which makes the change of the algorithm slow in the early stage and fast in the later stage.Then,the size of the particle swarm is controlled by contraction and expansion,and its discrimination is combined with the aggregation degree and diversity function of the particles.In the early stage,the contraction and expansion coefficient of the algorithm is at a large position,and in the later stage,the speed should be slowed down to strengthen the local search ability of the algorithm.Finally,the relationship between positioning error and particle fitness function is established.The experimental simulation shows that the proposed algorithm has better convergence performance,and can effectively suppress the influence of ranging error on location and improve the positioning accuracy of nodes compared with other algorithms.