摘要
为了减小无线传感器网络节点的定位误差,将粒子群优化算法应用于定位中,与以往的适应度函数来源不同,为解决误差累积问题,在最小二乘原理基础上采用加权系数,确定适应度函数的表达式。在粒子群优化算法中引入三种不同的参数组合观察不同的参数对迭代次数以及定位精度的影响,然后通过两种不同的适应度函数对定位误差进行比较。实验结果表明,合适的参数选择能降低算法的复杂度,新的适应度函数更能减小定位误差。
in order to reduce the positioning error of wireless sensor network node,the particle swarm optimization algorithm is applied to locate,unlike previous source of fitness function,to solve the problem of error accumulation,based on the principle of minimum squares using the weighted coefficient,to determine the fitness function expression. In particle swarm optimization algorithm is introduced in three different parameters combination of observation of different parameters on the number of iterations and the influence of the positioning accuracy,and then through two different fitness function positioning error in the comparison. The experimental results show that the suitable parameter selection can reduce the complexity of the algorithm,a new fitness function can reduce the positioning error.
出处
《网络安全技术与应用》
2013年第12期18-19,共2页
Network Security Technology & Application
基金
辽宁省科技厅资助项目20131045
辽宁省教育厅资助项目L2012218
关键词
无线传感器网络
粒子群优化
最小二乘
适应度函数
wireless sensor network（WSN）
Particle swarm optimization
Least squares
Fitness function
