基于智能算法的无人集群防御作战方案优化方法

兵力部署与任务分配是无人集群防御作战的重要过程，有效利用集群中有限的兵力并发挥出最高的作战效能对提高无人集群的作战胜率至关重要，进行高效的作战任务分配能够协调集群一致性并更好完成作战任务。针对无人集群防御作战中的关键作战方案，研究无人集群防御作战的兵力部署及协同任务分配优化问题。构建基于智能体技术的无人集群防御作战模型，量化无人集群兵力部署所需的关键参数，对作战区域与兵力进行规划，设计目标函数。提出一种自适应遗传算法，解决无人集群的兵力部署问题。算法可根据实时运行情况动态调整目标函数、交叉率和变异率，保证适应度值较高个体的传承并避免算法出现局部最优。进行防御作战仿真，为验证无人集群兵力部署的效果，提出一种基于深度Q网络的深度强化学习改进算法，解决无人集群任务分配问题，对部署好的无人集群进行任务分配并作战。该算法能够自适应调整Q值，避免算法因过度估计造成无法收敛至最优解。防御作战实验结果表明，所提出的无人集群兵力部署及协同任务分配方法可有效提高防御作战的成功率，实现无人集群的自主协同及智能对抗。

Optimization Method of Unmanned Swarm Defensive Combat Scheme Based on Intelligent Algorithm

Troop deployment and task allocation are important processes of unmanned swarm defense operations, it's very important to make effective use of the limited forces in the swarm and wield the highest operational efficiency to improve the battle victory rate of unmanned swarm. At the same time, efficient combat task allocation can coordinate the consistency of swarm and better complete combat tasks. Aiming at the key combat plan in unmanned swarm defense operations, the optimization of troop deployment and coordinated task allocation in unmanned swarm defense operations is studied. A multi-agent-based unmanned swarm defensive combat model is built to quantify the key parameters required for the deployment of unmanned swarm forces, the model plans the combat area and troops, and designs the objective function. An adaptive genetic algorithm is proposed to solve the deployment problem of unmanned swarms. The proposed algorithm could dynamically adjust the objective function，crossover rate and mutation rate according to the real-time operating conditions，ensuring the inheritance of individuals with higher fitness values and avoiding the local optimization of the algorithm. A defensive operation is simulated to verify the deployment effectiveness of unmanned swarm forces. The improved deep reinforcement learning algorithm based on deep Q network is proposed to find a solution to the task allocationfor the deployed unmanned swarms. The proposed algorithm could adjust the Q value through self-adaption to avoid non-convergence caused by the algorithm's overestimation and find the optimal solution.The experimental results of defensive operations show that the proposed unmanned swarm force deployment and coordinated task allocation method could effectively improve the success rate of defensive operations，and realize the autonomous coordination and intelligent confrontation of unmanned swarms.