基于对抗的突击武器与支援武器协同火力打击决策方法

为满足多类型武器协同火力优化打击的需求，提出了一种基于对抗的突击武器与支援武器协同火力打击决策方法。以突击武器“点对点”打击和远程火力支援武器“面杀伤”的协同为研究对象，考虑具有对抗特性的火力打击决策优化过程，以突击武器对目标的打击决策、目标对突击武器的打击决策以及支援武器的炮弹落点位置为优化变量，建立了以对抗双方剩余价值比值为目标函数的协同火力打击决策优化模型。提出了基于人工蜂群算法双层迭代优化的协同火力打击决策优化模型求解方法。目标分配决策变量采用整数编码，利用罚函数方法处理约束条件，将决策模型转化为无约束混合整数优化问题；针对算法实现过程，分析了双层迭代人工蜂群求解算法的计算复杂度。通过一个协同火力打击算例验证了协同火力打击决策模型和求解算法的合理性和有效性。

Confrontation-based Cooperative Fire Strike Decision-making Method of Assault Weapons and Support Weapons

A decision-making method for the cooperative fire strike (CFS) of assault weapons and support weapons in confrontation is proposed. And a decision-making model for CFS is established based on the ratio of friend or foe's residual values by studying the “point to point” strike of assault weapons and the “area damage” of long-range firepower support weapons, and the optimization process of decision-making of fire strike is considered. The decision of the assault weapons attacking the targets, the decision of the targets attacking the assault weapons and the drop points of projectiles launched from supporting weapons are taken as the optimization variables in decision-making model. A two-level iterative optimization method based on artificial bee colony (ABC) algorithm is proposed to solve the CFS decision-making optimization model. The integer is used to encode the decision variables, and the penalty function method is used to deal with the constraints. The decision-making model is transformed into an unconstrained mixed integer optimization problem. In view of the implementation process of the proposed algorithm, the computational complexity of the two-level iterative ABC algorithm is analyzed. A CFS example is used to verify the rationality and effectiveness of the collaborative fire strike decision-making model and the solving algorithm.