Topology Optimization Based on Adaptive Hummingbird Algorithm in Flying Ad hoc Networks
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摘要: 针对飞行自组网(FANET)中无人机(UAVs)快速移动造成的网络拓扑管理困难问题，考虑实际场景中无人机位置变化引起的可用信道差异，该文提出一种自适应蜂鸟算法对网络拓扑进行优化。首先，建立一个针对分簇结构的无人机拓扑模型，并且形成一个以最小化簇数量、负载偏差和簇移动度为目标的优化问题。其次，通过调节人工蜂鸟的觅食动作、加入扰动变异的方式，提出寻优能力更强的自适应蜂鸟算法(ADHA)。然后，设计合理的蜂鸟个体编码方式，将拓扑优化的决策过程转化为自适应蜂鸟算法的寻优过程。最后，通过仿真验证所提算法的收敛性，并与基于其他群智能优化算法的拓扑优化方法进行对比。实验结果表明，所提算法得到的拓扑优化策略不仅能够有效减少网络拓扑的簇数量，而且能够得到负载均衡、结构稳定的簇群。Abstract: To solve the network topology management difficulties caused by the rapid movement of Unmanned Aerial Vehicles (UAVs) in the Flying Ad hoc NETworks (FANET), an adaptive hummingbird algorithm is proposed to optimize the communication topology, which considers differences in available channels caused by the change of UAVs position in practical applications. Firstly, a UAV topology model for the clustered structure is established, and an optimization problem is formed to minimize the number of clusters, load deviation, and cluster mobility. Secondly, by adjusting the foraging action of artificial hummingbirds and adding disturbance variation, an ADaptive Hummingbird Algorithm (ADHA) with a stronger search ability is proposed. Thirdly, a reasonable hummingbird individual coding method is designed, and the decision-making process of topology optimization is transformed into the optimization process of ADHA. Finally, the convergence of the proposed algorithm is verified by simulation, and it is compared with other topology optimization methods based on other swarm intelligence optimization algorithms. The experimental results show that the topology optimization strategy obtained by the proposed algorithm can not only effectively reduce the number of clusters in the network topology, but also obtain clusters with balanced load and stable structure.
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表 1 仿真参数设置
仿真参数 参数数值 部署区域 50 km×50 km 无人机数量 50～300 最大通信半径 5～15 km 总信道数量 5, 10, 15, 20 移动模型 Random-way point 移动速度 30～50 m/s 
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表 2 算法参数设置
算法 参数设置 SSA PD = 20%, ST = 0.8, SD = 10% WHO PC = 0.2, PS = 0.13 AHA MC = 0.5N ADHA MC = 0.5N, Pmax = 0.5, Pmin = 0.1
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