云计算下的基于改进的蝙蝠算法的任务调度研究

Study on Task Scheduling Based on Improved Bat Algorithm Under Cloud Computing

针对云计算任务调度中存在效率低,提出了基于改进的蝙蝠算法(improved bat alogrithm,IBA)云任务调度。首先,建立了以执行时间和负载均衡的调度模型,其次在蝙蝠算法的初始化中采用混沌映射,提高了种群的多样性;在蝙蝠算法的自适应参数使用指数递减因子代替;在每一次迭代后使用量化正交交叉算子进行个体筛选。最后,在仿真实验中,IBA算法相比于蚁群算法、粒子群算法,蝙蝠算法都具有较好的调度效果。

Aiming at the low efficiency in cloud computing task scheduling,this paper proposes cloud task scheduling based on the Improved Bat Algorithm(IBA).First,a scheduling model based on execution time and load balancing was established;second,chaotic mapping was used in the initialization of Bat Algorithm to increase the diversity of the population;the adaptive parameters in Bat Algorithm are replaced by exponential decrease factors;after each iteration,the quantized orthogonal crossover operator is used for individual screening.Finally,in the simulation experiment,the IBA algorithm has better scheduling effects than ant colony optimization,Particle Swarm optimization,and Bat Algorithm.