Multi-objective Optimization of the Tether-Net Deployment Stage

The tether-net deployment stage involves a multi-objective optimization problem. A combinatorial optimization method is proposed to design the shooting parameters of a quadrilateral tether-net. Firstly, the mass-spring-damper method is used to model a tether-net, and the prediction model of the tether-net deployment stage is, for the first time, established based on a BP neural network. Comparing the outcomes of the model against the simulation results reveals that the mean absolute percentage error of the prediction model is less than 3%. Meanwhile, the influence of shooting velocity, shooting angle and bullet mass on the deployment performance of the tether-net is studied using the prediction model. Secondly, the deployment performance depends on many competing criteria, so the nondominated sorting genetic algorithm II is used to optimize the deployment stage, and the Pareto-optimal set is obtained. Finally, to obtain the optimal compromise solution, the technique for order preference by similarity to ideal solution is applied to the Pareto front according to the mission requirements. Comparison of three different cases shows that the combination of the optimization method proposed in this study is feasible and effective. The shooting angle has the most significant influence on the deployment performance, and the deployment distance is positively correlated with the effective distance.