基于自适应ACO的多约束QoS路由研究  

Study of multi-constrained Qo S routing based on adaptive Ant Colony Optimization

作　　者：杨坚[1] 彭玉旭[1] 

机构地区：[1]长沙理工大学计算机与通信学院,长沙410114

出　　处：《计算机工程与应用》2015年第13期106-110,共5页Computer Engineering and Applications

基　　金：湖南省自然科学基金(No.14JJ7043)

摘　　要：为了解决无线传感器网络Qo S(Quality of Service,Qo S)路由在寻找最优路径时要满足时延、抖动、能量等多个约束条件的问题,提出一种新的自适应蚁群优化算法,该算法有两方面的自适应策略。将信息素挥发因子ρ设置为动态自适应,在自适应因子μ作用下动态变化,增强算法的寻优能力,避免算法陷入局部最优;以多约束为条件建立加权的适应度函数,通过适应度函数值与自适应因子μ共同影响路径上的信息素更新,增强算法的收敛速度。通过仿真实验表明,该算法在满足多约束条件方面具有良好的效果。In order to solve problem of multi-constrained Qo S（Quality of Service,Qo S）routing of wireless sensor networks,which needs finding optimal path to satisfy multi-constraints,such as delay,jitter,energy issues,this paper proposes a novel adaptive Ant Colony Optimization algorithm.The algorithm has two kinds of adaptive strategy.Firstly,the pheromone evaporation factor is set to be dynamic and adaptive.The pheromone evaporation factor changes dynamically under adaptive factor,enhancing the searching capability of the algorithm,to avoid getting into a local optimum.Secondly,a weighted fitness function is created under condition of multi-constraints.By using the fitness function value combined with adaptive factor to affect the pheromone updating on the path can enhance the convergence speed of the algorithm.Simulation results show that the algorithm has a good effect in terms of meeting multiple constraints.

关 键 词：自适应蚁群算法 多约束条件 服务质量(Qo S)路由 信息素挥发因子 适应度函数 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]