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Multi-stage weapon target assignment (MSWTA) problem is usually treated as a classical constrained multi-objective optimization problem, which tends to balance the attack efficiency and economic loss for weapons after adding engagement resource constraints. In the context of multi-stage confrontations, the MSWTA becomes more complex and challenging to solve. To handle this constrained problem, this paper develops the advantageous two-archive evolutionary algorithm (C-TAEA) to combine the density estimation mechanism with a fast non-dominated sorting method to obtain solutions to this problem efficiently, while maintaining the convergence-oriented archive (CA) to speed up the generation of solutions and the diversity-oriented archive (DA) to enhance the diversity of populations. Extensive experiments are conducted to compare the proposed C-TAEA with the traditional NSGA-II according to the average of IGD and HV values. The numerical simulation results demonstrate that our method intuitively shows the performance improvement after adding the feasibility and can find the approximate Pareto Front more effectively.

In this paper, the two order quasi-newton iterative learning control algorithm for the multi-actuators vibration test system is investigated. First, a vibration coupling system with multi-actuators is established based on the frequency response function of the test beam. Second, a proportional-resonant filter is employed to filtrate the measurement disturbance in the system. Third, a novel two order iterative learning control algorithm based on he quasi-newton method is proposed to remove the actuate force coupling. The proposed control algorithm not only resolve the coupling phenomena effectively, but also guarantee the tracking errors of the system convergence with parameter uncertainty. Finally, simulations with eight actuators mathematical model with uncertainty are used to demonstrate the effectiveness of the proposed control algorithm.