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摘要:
针对无人机（UAV）编队的协同多任务分配问题（CMTAP），考虑双机协同探测、双机协同攻击的情况，结合时间约束、时序约束、时间间隔约束、载机弹药约束、任务能力约束等约束条件，扩展了协同多任务分配模型；将差分进化（DE）算法、郭涛（GT）算法、离散粒子群优化（DPSO）算法、模拟退火（SA）算法进行融合，提出了DE-DPSO-GT-SA算法，用以求解协同多任务分配问题。通过与多种算法进行比较，仿真试验结果表明，所提算法具有较好的收敛性能。
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关键词:
- 协同任务分配 /
- 协同作战 /
- 离散粒子群优化(DPSO) /
- 差分进化(DE) /
- 无人机(UAV)编队
Abstract:Aimed at Cooperative Multi-Task Assignment Problem (CMTAP) of Unmanned Aerial Vehicle (UAV) formation, considering the situation of cooperative detection and attack of two aircraft, combining the constraint conditions of time constraint, time sequence constraint, time interval constraint, ammunition constraint and task capability constraint, the model of cooperative multi-task assignment is extended, and a mixed Differential Evolution (DE) and Guo Tao (GT) algorithm, Discrete Particle Swarm Optimization (DPSO) algorithm and Simulated Annealing(SA) algorithm are fused to propose a DE-DPSO-GT-SA algorithm to solve the cooperative multi-task assignment problem. The performance of DE-DPSO-GT-SA algorithm and that of other algorithms are compared. Simulation results show that DE-DPSO-GT-SA algorithm has better convergence performance than other algorithms.
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图 5 5种算法的最优迭代过程比较
Figure 5. Comparison of optimal iterative process among five algorithms
表 1 飞机作战能力
Table 1. Combat capabilities of aircraft
飞机编号 起始位置/m 航程/m 导弹数目 速度/(m·s-1) 任务能力 探测 攻击 评估 1 (1 000, 500) 100 000 4 200 √ √ √ 2 (500, 1 000) 100 000 4 200 √ √ 3 (1 000, 1 000) 100 000 2 300 √ √ √ 4 (0, 1 000) 100 000 4 300 √ √ 5 (1 000, 0) 100 000 8 300 √ √ 6 (0, 0) 100 000 4 200 √ √ 
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表 2 任务目标点信息
Table 2. Information of task target points
目标编号 目标位置/m 执行任务所需飞机架数 探测 攻击 评估 1 (10 000, 10 000) 1 1 1 2 (20 000, 20 000) 2 1 1 3 (14 000, 6 000) 0 1 1 4 (8 000, 16 000) 0 1 1 5 (20 000, 10 000) 1 1 0 6 (10 000, 20 000) 1 2 1 7 (15 000, 15 000) 2 2 1 8 (8 000, 5 000) 0 1 0 9 (1 000, 3 000) 1 0 0 10 (5 000, 8 000) 1 1 0
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表 3 导弹和航程使用情况
Table 3. Missile and distance of aircraft
飞机编号 携带的导弹数目 已用导弹数目 航程/m 飞行距离/m 1 4 3 100 000 51 628 2 4 1 100 000 46 126 3 2 1 100 000 80 654 4 4 2 100 000 61 454 5 8 1 100 000 71 654 6 4 1 100 000 38 302
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表 4 5种算法综合比较
Table 4. Comprehensive comparison of five algorithms
算法 适应度值 平均运行时间/s 最优值 最劣值 平均值 DPSO 0.067 8 0.057 0 0.060 2 91.834 0 DE 0.075 3 0.062 9 0.067 2 99.549 4 DPSO-GT 0.078 1 0.062 5 0.068 5 222.813 6 DPSO-GT-SA 0.079 4 0.063 4 0.070 6 355.067 3 DE-DPSO-GT-SA 0.080 2 0.065 2 0.071 8 359.244 7
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