分簇和多目标自适应的和声搜索定位算法  

Localization Algorithm Based on Cluster and Multi-objective Adaptive Harmony Search for Wireless Sensor Networks

作　　者：孙子文[1,2] 申栋 孙崇[1] 

机构地区：[1]江南大学物联网工程学院,江苏无锡214122 [2]物联网技术应用教育部工程研究中心,江苏无锡214122

出　　处：《小型微型计算机系统》2017年第12期2719-2723,共5页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(61373126)资助;江苏省自然科学基金项目(BK20131107)资助;中央高校基本科研业务费专项资金项目(JUSRP51510)资助

摘　　要：针对集中式多目标优化定位算法计算复杂、容易陷入局部最优等问题,采用一种基于分簇和多目标自适应和声搜索分布式无线传感器网络定位算法.将无线传感器网络节点进行分簇定位,建立局部多目标定位模型,其目标函数为根据簇内节点间距离信息构建的空间约束目标函数,以及根据拓扑关系构建的拓扑结构约束目标函数,使用多目标自适应和声搜索算法进行定位,以解决多目标定位算法容易陷入局部最优问题,其中和声记忆库更新方法采用非劣排序和拥挤距离排序方法.仿真结果表明本文的定位算法,与PAES定位算法相比有较高的定位精度.To solve the problem of high complexity and trapping in local optimal solution exiting in centralized multi-objective optimi- zation localization algorithm, this paper proposes a distributed localization algorithm based on cluster and multi-objective adaptive harmony search for wireless sensor networks. In the proposed algorithm, the wireless sensor network nodes are clustered, furthermore the nodes are localized in different clusters. Each cluster establishes a local multi-objective localization model by using two objective functions：one is the space constraint object function consisting of distance information between nodes, the other is the topological constraint object function consisting of the topological relations between the nodes. A multi-objective adaptive harmony search algorithm is adopted to solve the problem of local optimal solution exiting in many multi-objective optimization algorithms for solving the problem of localization,in which the non-dominated sorting and crowding distance sorting algorithm are used to update the harmony memory. The simulation results show that the proposed location algorithm has higher location accuracy compared with the that of the PAES location algorithm.

关 键 词：无线传感器网络 定位 分簇 多目标自适应和声搜索算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]