物流云服务下基于改进蝙蝠算法的任务调度

Task scheduling based on improved bat algorithm under logistics cloud service

针对物流云服务模式中调度任务多、信息量大、需求广的特点,提出了一种改进蝙蝠算法求解物流云服务调度问题的方案,其优化目标为最小化调度时间和最大化资源利用率。根据设计的算法流程,首先基于工件升序排列(ranked order value,ROV)规则对蝙蝠个体进行重新编码;然后调整初始化数据范围来减少分配任务超载和资源闲置现象,并在迭代过程中增加约束条件来均衡任务量,最终实现了资源与任务的智能调度。通过和遗传、粒子群以及基本蝙蝠算法的对比分析,体现了改进算法的优越性。最后利用Witness对方案进行仿真,证明了改进蝙蝠算法在解决物流云服务任务调度中的有效性,同时扩展了蝙蝠算法的应用领域。

Considering the characteristics of multi-tasks ,large amount of information and wide demand, this paper put forward a proposal based on improved bat algorithm （IBA） to minimize the scheduling time and maximize the resource utilization. According to the algorithm process,it recoded the bat individuals based on the rules of ROV, adjusted the range of initialized date to reduce the overloaded task assignment and resource idly as well as increase the constraint conditions during the iterative process. All those realize the intelligent scheduling of logistics task. The comparison with genetic algorithm （ GA ）, particle swarm optimization （PSO） and standard bat algorithm reflected the excellence of IBA. Finally the successful simulation through Witness proves the effectiveness of IBA in task scheduling of logistic cloud service and extends the application field of IBA.