基于多种群遗传算法的多UUV任务分配方法

Task Assignment Method for Multiple UUVs Based on Multi-population Genetic Algorithm

多无人水下航行器(UUVs)协同侦察任务分配方案的优劣关系到作战效能甚至任务成败。文中针对传统遗传算法存在过早收敛、效率不高等问题,提出一种人机融合的多种群遗传算法,用于多基地、多目标、多约束的多UUV协同侦察任务分配。该算法通过引入多种群对解空间进行协同搜索,可更好地平衡全局寻优和局部搜索能力,突破经典遗传算法仅靠单种群进行寻优的性能瓶颈;另外,以人类先验知识作为启发信息辅助种群进行初始化,提高算法收敛效率;引入“遗忘策略”,缓解可能出现的进化不完全问题。基于典型想定进行仿真实验,结果表明,提出的多种群遗传算法具有较强的鲁棒性和较高的寻优效率,可以得到高品质的协同任务分配方案。

The quality of task assignment schemes for multiple unmanned undersea vehicles(UUVs) during cooperative reconnaissance is key to the operational effectiveness or even the success of missions. A multi-population genetic algorithm based on man-machine fusion was proposed for task allocation of multi-base, multi-target, and multi-constraint cooperative reconnaissance to solve the problems of premature convergence and low efficiency of traditional genetic algorithms. The algorithm can better balance global optimization and local search and break through the performance bottleneck of the classical genetic algorithm, which relies only on a single population for optimization, by introducing multiple populations to search the solution space cooperatively. In addition, human prior knowledge was used as heuristic information to assist population initialization in improving the convergence efficiency of the algorithm, and a forgetting strategy was introduced to alleviate possible incomplete evolution. The results of the simulation based on typical scenarios show that the proposed multipopulation genetic algorithm is robust, has high optimization efficiency, and can generate high-quality collaborative task allocation schemes..