基于改进遗传算法的多无人机协同任务分配
首发时间:2021-01-25
摘要:建立了以完成任务的收益、航程代价及时间代价作为目标函数,考虑异构无人机弹药资源、最大航程、任务执行能力、任务时序等约束,更符合实际作战情况的任务分配模型。提出基于多类型基因编码的自适应遗传算法,算法采用保证无人机异构性、可执行性、满足任务时序约束的无死锁编码,采用非线性自适应调节的遗传算子提高算法的收敛性和鲁棒性,并采用部分种群初始化的种群更新机制避免算法陷入局部最优。仿真结果表明,改进的算法对求解任务分配问题具有更好的优化性能。
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Multi-UAV Task Allocation based on Improved Genetic Algorithm
Abstract:A task allocation model is established, which takes the revenue, voyage cost and time cost of completing the task as the objective function, and considers the constraints of ammunition resources, maximum voyage, task execution ability and task sequence of heterogeneous UAV, which is more in line with the actual combat situation. An adaptive genetic algorithm based on multi type gene coding is proposed. The algorithm adopts deadlock free coding to ensure the heterogeneity, executability of UAV and meet the constraints of task timing. The genetic operator with nonlinear adaptive adjustment is used to improve the convergence and robustness of the algorithm, and the population update mechanism of partial population initialization is used to avoid the algorithm falling into local optimum. The simulation results show that the improved algorithm has better optimization performance for solving the task allocation problem.
Keywords: task assignment heterogeneous multi-UAVs adaptive genetic algorithm
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