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针对装备精确保障任务规划中任务时序逻辑约束和资源占用冲突等问题,建立以时效优先为目标的数学模型,提出基于多维动态列表规划和混沌蝙蝠算法的混合任务规划方法.通过多维动态列表规划选择处理的任务,设计具有自适应搜索策略和变异操作的离散混沌蝙蝠算法为选定任务分配资源.全局搜索中自适应调整惯性权重和学习因子以达到探索和开发能力的最佳平衡,局部搜索中采用混沌变异操作协助种群跳出局部最优.仿真算例表明,所提算法具有较快的收敛速度和较高的求解精度.
Aiming at the problems of time-bound logical constraints and resource occupation conflicts in task plan of equipment precise support, a mathematic model with time-first priority is established and a hybrid task planning method based on multidimensional dynamic list programming and chaotic bat algorithm is proposed. Processing tasks, a discrete chaotic bat algorithm with adaptive search strategy and mutation operation is designed to allocate resources for the selected tasks.The global search adaptively adjusts the inertia weight and the learning factor to achieve the best balance of exploration and development capabilities. In the local search The chaotic mutation operation is used to help the population to jump out of the local optimum. Simulation results show that the proposed algorithm has faster convergence speed and higher precision.