基于自适应GWO的多UCAV协同攻击目标决策

针对多架无人机相互协同攻击多个来袭目标的武器目标决策问题进行了研究。利用层次分析法（AHP）评估了空战能力指数和三维空战态势威胁指数的权重，针对协同攻击空战的分配原则，采用计算分配值的情况下提出了一种自适应搜索的灰狼求解算法，实现了武器目标攻击决策的求解。仿真表明，改进的GWO算法对决策方案的求解速度和求解质量与现有的粒子群算法（PSO）、蚁群算法（ACA）和遗传算法（GA）等相比均有所明显提高。

Research on cooperative attack decision of Unmanned Combat Aerial Vehicles using self-adaptive Grey Wolf Optimization

The target assignment decision-making for Cooperative Attack on Multiple Targets（CAMT） is investigated in this paper. Analytical Hierarchy Process（AHP） is applied to evaluate the weight values of air combat capability and 3D air combat situation for threats. There is a situation that for assignment principles of cooperative air combat, the assignment value is calculated. According to this circumstance, a self-adaptive Grey Wolf Optimization（GWO） is proposed to the CAMT problem. Simulation results show that the self-adaptive GWO proposed in this paper for solving the speed and quality of solutions outperforms significantly the existing Particle Swarm Optimization（PSO）, Ant Colony Algorithm（ACA） and Genetic Algorithm（GA）.