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在云计算环境中用户数量巨大,需要处理的任务繁多,高效的任务调度算法是云计算需要解决的关键问题之一。针对云计算的模型结构,引入粒子群算法和蚁群算法联合优化任务调度算法。首先使用粒子群算法生成初始调度结果,并引入随机性的惯性权重提高算法的调节能力,将改进粒子群算法生成的结果作为蚁群算法的初始信息素寻找最优调度方案,并使用遗传算法中的精英策略和交叉算子改进蚁群算法,在算法中使用多层次优化算法提高算法运行效率。实验结果表明,在相同的条件下,改进后的算法任务总完成时间得到降低,且随着任务量的增加性能优势更为明显。
In the cloud computing environment, there are a large number of users and a large number of tasks to be handled. An efficient task scheduling algorithm is one of the key problems that cloud computing needs to solve. Aiming at the model structure of cloud computing, particle swarm optimization algorithm and ant colony algorithm are introduced to optimize the task scheduling algorithm. Firstly, particle swarm optimization is used to generate the initial scheduling results, and the stochastic inertia weight is introduced to improve the adjusting ability of the algorithm. The improved particle swarm optimization algorithm is used as the initial pheromone to find the optimal scheduling scheme. Genetic algorithm The elite strategy and crossover operator improved ant colony algorithm, using multi-level optimization algorithm in the algorithm to improve the efficiency of the algorithm. The experimental results show that under the same conditions, the total task completion time of the improved algorithm is reduced, and the performance advantage becomes more obvious as the workload increases.