装备精确保障任务规划建模与混沌蝙蝠算法求解

Task scheduling modeling and chaotic bat algorithm solving method of equipment efficient support

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,为选定的任务分配资源.全局搜索中自适应调整惯性权重和学习因子以达到探索与开发能力的最佳平衡,局部搜索中采用混沌变异操作以协助种群跳出局部最优.仿真算例表明,所提出算法具有较快的收敛速度和较高的求解精度.

For the problems of task sequential logic constraints and resource occupancy conflicts among equipment efficient support task scheduling, a mathematical model in pursuit of the priority of task implementation time is established,a hybrid task scheduling method based on multi-dimensional dynamic list scheduling（MDLS） and the chaotic bat algorithm is proposed. In the proposed method, the task to be disposed is selected by MDLS, then the discrete chaotic bat algorithm（DCBA） with the adaptive searching strategy and mutation operator is designed to allocate the resource to the selected task. Inertia weight and acceleration coefficients are adjusted adaptively in global search to coordinate the exploration and exploitation ability, and the chaotic mutation operator is adopted in local search to help the swarm jump out from local optimum. The simulation example illustrates that the proposed method has better performance in convergence speed and solving precision.