基于蚁群优化算法的云计算任务分配

针对已有的适用于分配任务的蚁群算法易陷入局部最优解的缺陷，提出了一个保证云服务质量的分组多态蚁群算法。该算法将蚁群按职能不同分为搜索蚁、侦察蚁和工蚁，根据预测完成时间的更新使平均完成时间逐渐取得最小值，从而减少产生局部最优解的可能，最后通过Cloudsim仿真实现。结果表明该方法减少了处理请求任务的平均完成时间，提高了任务处理的效率。

Task allocation based on ant colony optimization in cloud computing

Concerning the defects of the Ant Colony Optimization(ACO) for the task allocation,a grouping and polymorphic ACO was proposed to improve the service quality.The algorithm,which divided the ants into three groups: searching ants,scouting ants and working ants,with the update of forecast completion time to gradually get the minimum of the average completion time and to decrease the possibility of generation to local optimum,was emulated and achieved with Cloudsim tookit at last.Results of the experiment show that the time of handling requests and tasks of this approach has been reduced and the efficiency of handling tasks gets improved.