基于改进二进制蝙蝠算法的装备资源保障规划

Equipment Resource Support Planning Based onImproved Binary Bat Algorithm

针对联合作战环境下的装备资源精确保障协同规划问题,考虑以所有保障任务完成时间最短为目标,以保障任务的执行时序和资源需求、保障编组占用冲突,以及资源平台能力更新机制等复杂条件为约束,构建数学模型,提出了基于动态列表调度(Dynamic List Scheduling,DLS)和自适应进化变异二进制蝙蝠算法(Adaptive Mutation Binary Bat Algorithm,AMBBA)的混合装备资源协同保障规划方法。通过动态列表调度选择当前执行保障的任务,在二进制蝙蝠算法寻优中引入自适应学习因子以平衡全局搜索和局部搜索能力,通过在当前可用资源集中搜索最优解为选定任务分配资源,以复杂地域联合作战为例仿真并验证规划效果,结果显示,所提方法可对大规模装备资源协同分配保障问题进行精确高效求解。

Aiming at the problem of precise support collaborative planning of equipment resources in the joint operation environment,regarding the shortest completion time of all support tasks as the goal,taking such complex conditions as the execution sequence and resource requirements of support tasks,the occupation conflict of support grouping and the capability update mechanism of resource platform as the constraints,a mathematical model is constructed.The cooperative support planning method of hybrid equipment resources based on DLS and AMBBA.First of all,the task of current execution support is selected by DLS,and then adaptive learning factor is introduced into the optimization of BBA to balance the global and local search ability.Then the optimal solution is searched in the current available resource set to allocate resources for the selected task.Finally,the planning effect is simulated and verified by the examples of complex regional joint operation and the results show that the proposed method is accurate and effective in solving the problem of large-scale equipment resource cooperative allocation support.