基于D-NSGA-GKM算法的多阶段武器协同火力分配方法

陆战场防御作战场景中的多阶段武器协同火力分配问题是典型的约束组合优化问题,其目的是生成合理有效的火力分配方案.为了更接近实际作战情况,引入双方对抗过程,建立包含敌方作战单元战场剩余价值、作战资源消耗、作战单元战场价值损失的武器火力分配模型.针对多阶段武器协同火力分配问题,在非支配排序遗传算法III(non-domina...

Optimization method for multi-stage collaborative weapon firepower distribution based on D-NSGA-GKM algorithm

The problem of multi-stage weapon collaborative firepower distribution in land battlefield defense is a typical constrained combination optimization problem, which aims to generate a reasonable and effective firepower distribution scheme. In order to get closer to the actual operational, the confrontation game process of both sides has been introduced and a weapon firepower distribution model including the residual value of enemy combat units, combat resource consumption, and battlefield value loss of combat units is established. An improved intelligent algorithm(D- NSGA-GKM) is proposed based on the non-dominated sorting genetic algorithm III(NSGA-III) for multi-stage collaborative weapon firepower distribution. Firstly, a non-dominated sorting algorithm based on the dominance degree matrix is introduced to reduce redundant operations in the sorting process to improve the efficiency of non-dominated sorting. Then, the repair operator is introduced in the genetic operation stage to repair the infeasible solution. Finally, the genetic K-mean clustering algorithm is introduced to cluster the initial reference points automatically, the centroid of the cluster is used to replace the original reference points, and the penalization-based boundary intersection distance is introduced in the environmental selection stage to replace the vertical distance, to improve the convergence of the algorithm. The experimental results show that the D-NSGA-GKM algorithm has excellent time performance and convergence performance on the problem of multi-stage weapon cooperative fire distribution.