长链树状无线传感器网络中遗传蚁群路由优化算法  

Cloud Genetic AntNet Algorithm Based Routing Optimization for Wireless Sensor Networks with Long-chain Tree Structure

作　　者：朱永利[1] 李丽芬[1] 

机构地区：[1]华北电力大学控制与计算机工程学院,河北保定071003

出　　处：《小型微型计算机系统》2012年第4期833-838,共6页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(60974125)资助

摘　　要：为提高长链树状无线传感器网络的服务质量(QoS),本文用云遗传蚁群网络算法对无线传感器网络路由进行优化.算法中将正向蚂蚁根据节点负载情况发现的可行路径作为遗传算法的初始种群进行染色体编码,用路径时延、跳数及链路质量定义的适应度函数对染色体进行评价;利用正态云发生器实现路径的交叉和变异操作,逆向蚂蚁对优化后的路径进行信息素更新.仿真结果表明该路由算法能够满足无线传感器网络的实时性、可靠性等方面的要求,实现了网络的负载平衡及拥塞控制机制.To improve QoS of wireless sensor networks with long chain tree-like topology, this paper proposes a new cross-layer rou- ting algorithm for wireless sensor networkscloud model based genetic ＆ AntNet routing optimization algorithm. The forwards ants search for the feasible paths based on the load value in the new algorithm. These paths are considered as the initial population of the genetic algorithm. The codings of the paths are considered as chromosomes. The fitness function of the path is defined with delay, hop count and packet reception rate. The Y-conditional cloud generator is used as the cross operator, and the basic cloud generator is used as the mutation operator. The backwards ants update the pheromone of the optimal paths. The simulation results demonstrate that the routing algorithm can guarantee the real time, reliability and robustness of wireless sensor networks. It can also achieve the network load balancing and congestion control mechanism.

关 键 词：无线传感器网络 QOS路由 云遗传算法 蚁群网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]