装备保障任务分配建模与DLS-BCIWBA算法求解

针对以时效优先为目标的装备精确保障协同任务分配问题,设计考虑保障单元能力更新机制,提出基于动态列表规划 (dynamic list scheduling,DLS)和二进制混沌入侵杂草蝙蝠算法 (binary chaotic invasive weed bat algorithm,BCIWBA)的混合任务分配方法,通过DLS选择所需执行的任务,设计BCIWBA为选定任务分配保障单元。BCIWBA利用蝙蝠算法的全局寻优能力和迭代初期快速收敛性进行全局搜索,然后选取部分最优个体融合入侵杂草生长繁殖、空间扩散和竞争生存机制进行局部搜索,并通过学习因子和惯性权重的自适应协同更新以平衡探索和开发能力,结合脉冲频率、响度和发生率变化区间的混沌搜索避免早熟收敛。仿真算例表明,所提方法可对时序逻辑任务分配问题进行快速高效求解。

Task allocation modeling and solving algorithm for equipment support using DLS-BCIWBA

For the problems of equipment efficient support cooperative task allocation, which needs to consider support capability renewal mechanism, a hybrid task allocation method based on dynamic list scheduling (DLS) and binary chaotic invasive weed bat algorithm (BCIWBA) is proposed. The task needed to dispose is selected by DLS, then BCIWBA is designed to allocate the support unit to the selected task. First, global optimization ability and fast convergence performance at early stages of the iterations of the bat algorithm is utilized in global search. Then, partially optimal individuals are selected to integrate into invasive weed growth and reproduction, spatial dispersal and competitive exclusion mechanism in local search. Meanwhile, in order to coordinate the exploration and exploitation ability, the inertia weight and leaning factor are adjusted adaptively. Finally, chaotic searching of frequency, loudness and pulse emission rate interval are conducted to avoid the premature convergence. The simulation example show that the proposed method has better performance in convergence speed and solving precision with sequential logic task allocation.