Research on UAV Ad Hoc network routing based on optimization algorithm

Neural Networks, Information and Communication Engineering Pub Date : 2022-06-30 DOI:10.1117/12.2639642

Wensheng Chi, Wen-jun Xie, Peng Zhang, Wenxuan Lin, L. Ru

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Abstract

One of the urgent problems to be solved in UAV cluster warfare is to find a suitable routing algorithm to adapt to the characteristics of UAV Ad Hoc network topology. Based on the further development of cellular network, the wireless Ad Hoc network does not need preset infrastructure, no control center, and can be temporary networked, which is more in line with the needs of unmanned aerial vehicle Ad Hoc network. In consideration of the premise of distributed deployment of unmanned aerial vehicle (UAV), matlab is used for simulation of opportunistic routing algorithm. As for multiple constraints Qos routing problem, tabu search algorithm is adopted to optimize the opportunistic routing algorithm. The experimental results show that, after intelligent algorithm is optimized, along with the increase of the time consumed for algorithm, other constraints impose effective impact on the algorithm, enabling the opportunistic routing algorithm make the optimal choice according to multiple constraints. Then genetic algorithm is used for comparison, and the conclusion that genetic algorithm can get faster convergence is drawn.

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基于优化算法的无人机Ad Hoc网络路由研究

针对无人机Ad Hoc网络拓扑结构的特点，寻找合适的路由算法是无人机集群战中亟待解决的问题之一。无线Ad Hoc网络在蜂窝网络的进一步发展基础上，不需要预置的基础设施，没有控制中心，可以临时组网，更符合无人机Ad Hoc网络的需求。在考虑无人机分布式部署的前提下，利用matlab对机会路由算法进行仿真。针对多约束Qos路由问题，采用禁忌搜索算法对机会路由算法进行优化。实验结果表明，智能算法优化后，随着算法耗时的增加，其他约束对算法产生了有效的影响，使得机会路由算法能够根据多个约束做出最优选择。然后用遗传算法进行比较，得出遗传算法收敛速度更快的结论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Neural Networks, Information and Communication Engineering

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