中文摘要：

蚁群优化ACO（Ant Colony Optimization）作为一种模拟进化算法,具有信息正反馈、分布式计算和多agent协同的特点,在求解复杂优化问题方面体现出许多优越性。提出基于ACO的无线自组织网络能量感知路由协议ABEAR（Ant-Based Energy-Aware Routing）。协议按需发送人工蚂蚁进行路由发现,根据信息素浓度、节点能量和链路使用情况综合选择下一跳节点来转发数据包,尽量避开信道使用频率较高的路径,还可根据节点通信活动情况将空闲节点转入睡眠状态来节省能量消耗。由于蚁群参数的取值对于ACO算法的性能有着非常重要的影响,因此在分析三个关键参数（信息素挥发系数ρ、信息素权重因子α、剩余能量和链路拥塞指标权重因子β）对ABEAR性能的影响基础上,在NS2平台上进行了仿真实验,对参数优化的效果进行了对比,并总结出了参数值设定的具体步骤。

英文摘要：

Ant colony optimisation is a simulated evolutionary algorithm which is characterised with a positive feedback,distributed computation and multi-agent synergy.It shows many advantages in solving complicated optimisation problems.This paper puts forward an ACO-based energy-aware routing protocol（ABEAR） for mobile Ad Hoc networks.ABEAR sends out artificial ants to find paths to the destination node reactively,selects comprehensively the next hop to forward data packets based on the pheromone density,the nodes energy and the link usage situation.ABEAR tries hard to make channel avoid the paths highly occupied and can make idle node turn to sleeping state to conserve energy according to the communication situation of nodes.The selection on parameters of the ACO algorithm plays an important role for the performance of the algorithm,therefore,in this paper,the influence of three key parameters,the pheromone evaporating factor ρ,the weight of pheromone α and the weight of the remaining energy link congestion metric β upon ABEAR are analysed,and the simulation experiments on NS2 platform are carried out.Comparison has been made between the effects of parameters optimisation,and specific parameters setting is summarised as well.