AdHoc中的一种改进的人工蜂群和粒子群算法的组播路由的研究

发布时间：2018-01-15 18:17

本文关键词：AdHoc中的一种改进的人工蜂群和粒子群算法的组播路由的研究　出处：《科技通报》2017年02期 　论文类型：期刊论文

【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。
[Abstract]:The multicast routing problem of Adhoc needs to consider the QoS constraints such as delay, bandwidth, cost and packet loss rate, which is essentially a NP problem. In this paper, artificial bee swarm algorithm and particle swarm optimization algorithm are mixed. The introduction of Cauchy distribution into the honeysource updating formula of honeybee colony algorithm can help the bees jump out of the local optimum and improve the speed of producing the global optimal solution. The use of Levi flight can reduce the time of finding the optimal solution. The idea of adaptive parameter transformation is adopted in particle swarm optimization (PSO) algorithm, which accelerates the optimal solution of population. This algorithm is applied to multicast routing algorithm in Ad Hoc network. On the premise of satisfying the QoS constraint function, the cost of generating the optimal routing solution is reduced. The simulation results show that the proposed algorithm is more effective than the basic artificial bee swarm algorithm and particle swarm optimization algorithm in network delay, network cost, network cost and packet loss rate.
【作者单位】：扬州大学信息学院;江苏联合职业技术学院扬州分院;
【分类号】：TN929.5;TP18
【正文快照】： Qo S路由组播技术是Adhoc网络中的研究关键技术之一,伴随着云计算等概念的应用发展,这就要求网络的的质量能够得到提高,在Ad Hoc网络中,目前很多的算法都是基于基于Bellman-Ford或者Dijkstra算法[1],无法能够为Qo S提供很好的支持。文献[2]提出一种基于寿命估算MMAS的Ad Hoc网

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