Ad Hoc网络重采样转移概率模型下激励传播算法

发布时间：2019-06-15 01:24

【摘要】：无线多跳Ad Hoc网络的激励传播算法可以优化网络资源负载均衡,提高网络可靠性。传统的激励传播算法采用萤火虫梯度搜索定位协作激励方法,当网络节点分布为随机多跳的异构网络时,激励传播负载均衡存在效率低、准确性不高等问题。提出一种基于萤火虫群优化追踪的重采样转移概率模型下的无线多跳Ad Hoc网络激励传播算法,实现资源负载均衡,设计无线多跳Ad Hoc网络覆盖层次模型,提出萤火虫群优化追踪算法,在无线多跳Ad Hoc网络节点转移概率空间,设计重采样转移概率模型,得到多跳Ad Hoc网络激励传播系统状态估计。仿真结果得到该算法实现无线多跳Ad Hoc网络系统状态估计准确率要明显高于传统算法,估计误差的波动小,在解决蜕化的同时避免了样本贫化,资源负载均衡激励精度提升方面明显优越于其它算法,展示了算法的优越性能。
[Abstract]:The excitation propagation algorithm of wireless multi-hop Ad Hoc network can optimize the load balance of network resources and improve the reliability of the network. The traditional incentive propagation algorithm adopts firefly gradient search location cooperative incentive method. When the network nodes are distributed into random and multi-hop heterogeneous networks, the incentive propagation load balancing has some problems, such as low efficiency, low accuracy and so on. In this paper, a wireless multi-hop Ad Hoc network excitation propagation algorithm based on resampling transfer probability model based on firefly cluster optimization tracking is proposed, which realizes resource load balancing, designs wireless multi-hop Ad Hoc network coverage hierarchy model, proposes firefly cluster optimization tracking algorithm, designs resampling transition probability model in wireless multi-hop Ad Hoc network node transition probability space, and obtains state estimation of multi-hop Ad Hoc network excitation propagation system. The simulation results show that the accuracy of state estimation of wireless multi-hop Ad Hoc network system is obviously higher than that of the traditional algorithm, the fluctuation of estimation error is small, the sample dilution is avoided, and the excitation accuracy of resource load balancing is obviously superior to other algorithms, which shows the superior performance of the algorithm.
【作者单位】：莱芜职业技术学院总务处;
【基金】：莱芜市科研项目(2013YA001)
【分类号】：TN929.5

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