摘要
Adhoc的组播路由问题需要考虑延迟、带宽、费用和丢包率等服务质量约束的问题,其本质是一个NP问题。本文将人工蜂群算法和粒子群算法进行混合,在蜂群算法的蜜源更新的公式中引入柯西分布,能够帮助探路蜂跳出局部最优,提高产生全局最优解的速度,使用列维飞行可以减少寻找优化解的时间,在粒子群算法中采用自适应参数变换的思想,加速了产生种群的最优解。本文算法应用在Ad Hoc网络的组播路由算法中,在满足Qo S约束函数的前提下,降低了产生路由最优解的费用,缩短了时间。仿真实验说明本文的算法与基本的人工蜂群算法和粒子群算法相比在网络时延,网络消耗费用,网络成本和丢包率方面具有很好的效果。
The issue of Adhoc’s multicast routing needs to take into account the issues of delay,bandwidth, cost, packet loss rate and other service quality constraints, and it is essentially an NP problem, This paper integrates the artificial bee colony algorithm with the particle swarm algorithm and introduces the Cauchy distribution into the honey source updating formula of the bee colony algorithm to help the pathfinding fee to jump out of local optimum and improve the speed of global optimal solutions.Using the Levi flight can reduce the time of searching for an optimal solution, and self- adaptive parameter transformation is adopted in the particle swarm algorithm to accelerate the speed of producing the optimal results. On the premise of meeting the QoS constraint functions, algorithm in this paper is applied to the multicast routing algorithm of the Ad hoc algorithm, which has reduced the cost of producing the optimal routing solution and shortened the time. Simulation experiment shows that compared with basic artificial bee colony algorithm and particle swarm algorithm, algorithm in this paper has good effects in terms of network delay, network consumption costs and packet loss rate.
作者
郭琪瑶
朱范德
Guo Qiyao;Zhu Fande(Information Engineering College,Yangzhou University,Yangzhou Jiangsu 225003,China;Yangzhou School,Jiangsu Union Occupation Technical Yangzhou,Yangzhou Jiangsu 225003,China)
出处
《科技通报》
北大核心
2017年第2期81-85,共5页
Bulletin of Science and Technology
关键词
Adhoc组播路由
柯西分布
列维飞行
自适应参数变换
QOS
ad Hoc multicast routing
cauchy distribution
levi flight
self- adaptive parameter transformation
QoS
