Target Allocation for Air Defence Weapons Based on D-A-NSGA-III

To address the current lack of clarity in the distribution of targeted munitions, we propose a multi-target weapon target allocation model that considers shooting efficiency and construct a novel algorithmic framework. In this framework, we introduced the dominance matrix and developed our own adaptive genetic selection mechanism, and proposed a non-dominated sorting genetic algorithm (D-A-NSGA-III) based on the dominance degree matrix and adaptive genetic selection mechanism. The model clearly defines the correlation between the probability of damage to the target and the amount of munitions allocated, based on the shooting efficiency, and establishes a clear munitions allocation strategy for each target with a corresponding weapon-target allocation scheme. D-A-NSGA-III improves the convergence speed of the algorithm by reducing redundant comparisons in the ranking scheme, effectively preserving the structure of the good non-dominated solutions and improving the inferior dominated solutions. The results of the numerical experiments show that D-A-NSGA-III reduces its convergence time by a maximum of 77.63% and a minimum of 20.09% under three different experimental scenarios compared to the non-dominated degree-ranked genetic algorithm based on the reference point mechanism(NSGA-III) and the non-dominated sorting genetic algorithm based on dominance matrix(D-NSGA-III). This significant improvement not only validates the reasonableness of the proposed model, but also fully demonstrates the superiority of the D-A-NSGA-III algorithm over other similar algorithms. This research not only provides an effective technical approach to unmanned air defence artillery operations, but also offers a new solution to the multi-objective optimisation problem.