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Improved Ant Colony Optimization for Weapon-Target Assignment

机译：改进的蚁群算法用于武器目标分配

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摘要

Weapon-target assignment (WTA) which is crucial in cooperative air combat explores assigning weapons to targets with the objective of minimizing the threats from those targets. Based on threat functions, there are four WTA models constrained by the payload and other tactical requirements established. The improvements of ant colony optimization are integrated with respect to the rules of path selection, pheromone update, and pheromone concentration interval, and algorithm AScomp is proposed based on the elite strategy of ant colony optimization (ASrank). We add garbage ants to ASrank; when the pheromone is updated, the elite ants are rewarded and the garbage ants are punished. A WTA algorithm is designed based on the improved ant colony optimization (WIACO). For the purpose of demonstration of WIACO in air combat, a real-time WTA simulation algorithm (RWSA) is proposed to provide the results of average damage, damage rate, and kill ratio. The following conclusions are drawn: (!?cmd??1) the third WTA model, considering the threats of both sides and hit probabilities, is the most effective among the four; (!?cmd??2) compared to the traditional ant colony algorithm, the WIACO requires fewer iterations and avoids local optima more effectively; and (!?cmd??3) WTA is better conducted when any fighter is shot down or any fighter's missiles run out than along with the flight.
机译：在合作空战中至关重要的武器目标分配（WTA）探索将武器分配给目标，目的是最大程度地减少来自这些目标的威胁。基于威胁功能，存在四种受有效载荷和其他战术要求约束的WTA模型。结合路径选择，信息素更新和信息素浓度区间的规则，对蚁群优化的改进进行了综合，提出了基于精英蚁群优化策略（ASrank）的算法AScomp。我们向ASrank添加垃圾蚂蚁；信息素更新时，奖励精英蚂蚁，惩罚垃圾蚂蚁。基于改进的蚁群算法（WIACO）设计了一种WTA算法。为了演示空战中的WIACO，提出了一种实时WTA模拟算法（RWSA），以提供平均伤害，伤害率和杀伤率的结果。得出以下结论：（！cmd ?? 1）考虑到双方的威胁和命中概率，第三个WTA模型是这四个模型中最有效的； （！cmd ?? 2），与传统的蚁群算法相比，WIACO需要更少的迭代，并且更有效地避免了局部最优。和（！cmd ?? 3）当战斗机被击落或战斗机的导弹用尽时，WTA的飞行效果要好于飞行。

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