基于改进遗传算法PFGA的WSN节点定位优化  

Optimized localization in WSN based on improved genetic algorithm PFGA

作　　者：许必宵 张欣慧[1] 徐晶晶[1] 宫婧[2] 孙知信[1] 

机构地区：[1]南京邮电大学物联网学院,江苏南京210003 [2]南京邮电大学理学院,江苏南京210023

出　　处：《南京邮电大学学报（自然科学版）》2016年第3期92-99,共8页Journal of Nanjing University of Posts and Telecommunications：Natural Science Edition

基　　金：国家自然科学基金(60973140;61170276;61373135);江苏省高校自然科学研究重大项目(12KJA520003)资助项目

摘　　要：在无线传感器网络节点定位过程中使用最小二乘法求解时,定位的精度会受距离误差所影响,遗传算法(GA)可以用来降低定位误差从而达到优化定位的目的。为了在维持适度能耗的同时提高定位精度,文中提出了一种遗传定位优化算法PFGA。该算法利用罚函数思想设计自适应度函数,然后针对每个未知节点,在合理区域中选取初始染色体并选用新型交叉方式与变异方式进行迭代,最终确定每个未知节点的位置。仿真结果表明,该算法能够对节点定位进行优化且效果明显,同时对比其他文献提出的定位算法,该算法收敛速度更快、定位精度更高。When the least square method is used in the node localization of wireless sensor network , the accuracy of localization will be affected by errors of distances. A genetic algorithm （GA） can be used to reduce errors and optimize results of the localization . In order to improve the accuracy of the localization with moderate consumption on energy , this paper proposes an improved genetic algorithm for the optimi- zation the localization, called the PFGA. The algorithm designs the adaptive degree function with penalty function firstly. For each unknown node, the algorithm selects initial chromosomes in the reasonable re- gion, takes new way to cross and mutate, and determines location of each unknown node ultimately. The simulation results show that the algorithm can optimize results of localization, and has faster convergence speed and higher precision compared with localization algorithms proposed in other literatures

关 键 词：遗传算法 节点定位 无线传感器网络 罚函数 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] TN92[自动化与计算机技术—计算机科学与技术]