Solving Multi-Stage Weapon Target Assignment Problems by C-TAEA

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.