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A routing prediction strategy via a pigeon-inspired optimization (PIO)-based neural network (NN) is designed for UAV swarm networks with highly dynamic topology. The proposed strategy can predict the performance of the neighboring nodes as the next hop. For more precise prediction and less computational complexity, the states of the UAV swarm motion and the network are considered as the prior information, and the PIO-based NN framework is established. Based on the system model, PIO is applied to find the optimal weight matrices of the NN-based routing prediction model. The matrix of the hop count index function is calculated using this prediction model. The proposed strategy can directly determine the next hop based on the prediction results or can be combined with other routing methods to maintain a balance between the stability and the shortest path. Numerical simulations are conducted to demonstrate the effectiveness of the proposed strategy.

Purpose Taking into account the factors of torque saturation and angular velocity limitation during the actual attitude maneuver of the satellite, as well as the difficulty of parameter selection in the design of attitude control algorithm, the purpose of this paper is to propose a satellite magnetic/momentum wheel attitude control technology based on pigeon-inspired optimization (PIO) cascade-saturation control law optimization. Design/methodology/approach The optimal parameters are calculated through the PIO algorithm and then the parameters are used in the cascade-saturation control law to control the actuator findings-mathematical simulation results show that the cascade-saturation control law optimization algorithm based on PIO can shorten the adjustment time and reduce the steady-state error. Findings Compared with traditional attitude maneuver control with given parameters, the PIO algorithm can accurately calculate the optimal parameters needed to achieve the control objective and this method has better stability and higher accuracy. Originality/value The innovative PIO algorithm is used to calculate the optimal parameters, and the cascade saturation control law is used to control the actuator. Compared with the traditional algorithm, the regulation time is shortened and the steady-state error is reduced.