In multi-UAVs system, high-quality communication system is the key to ensure cooperation. Most of the existing methods focus on the trade-off between connectivity and the number of network edges. However, for a multi-UAVs communication network with high quality of service (QoS), more metrics of communication network need to be considered, such as the hops and the maximum number of links. In this paper, we propose a multi-objective optimization method for dynamic communication network. Firstly, in order to accurately estimate the moved positions with random noise, Long and Short Term Memory network (LSTM) is used to predict succeeding positions of all UAVs. Secondly, Deep Q-network (DQN) with random action space is designed to adaptively adjust the communication network. In particular, it can be applied to large-scale communication networks. Furthermore, we propose a transfer learning method based on the trained LSTM and DQN. In this transfer learning, these two models which are well-trained in smaller communication network and combined with principal component analysis (PCA) can be directly used for communication network optimization of larger networks. Experimental results show that our method is not only superior to other methods but also faster in convergence.

Highly robust networks can resist attacks, as when some UAVs fail, the remaining UAVs can still transmit data to each other. In order to improve the robustness of a multi-UAV network, most methods construct the network by adjusting the positions of the UAVs and adding a large number of links. However, having a large number of links greatly consumes communication resources and increases serious signal interference. Therefore, it is necessary to study a method that can improve robustness and reduce the number of links. In this paper, we propose a method that consists of combining formation control and link selection, which can work in a distributed manner. For formation control, our method keeps the UAVs compact in the obstacle environment through an improved artificial potential field. The compact formation enables UAVs to have a large number of neighbors. For link selection, reinforcement learning is used to improve the robustness of the network and reduce the number of network edges. In the simulation of the 3D urban environment, three failure modes are used to verify the robustness of the network. The experimental results show that even if the number of links is reduced by $20\%$, the networks designed by our method still have strong robustness.