基于动态粒子群算法的工作流服务主体优选方法

Optimization Method of Workflow Service Subject Based on Dynamic Particle Swarm Algorithm

在研究工作流服务时间-费用双重优化问题的基础上,提出一种基于动态粒子群算法的工作流服务主体优选方法。通过区域划分,在每个粒子所在区域内,当适应值小于最佳适应值时,对区域重新进行初始化,从而使算法具有更强的全局收敛性和动态的自适应性;同时引入随机扰动、回退等算子,将搜索范围扩大到整个解空间以大大提高获得最优解的概率。结合动态粒子群算法建立工作流调度问题的目标模型,并从跨时间粒度、跨时区、跨工作时间3个方面对工作流服务主体优选方法进行了讨论分析。实验结果表明,该方法比其他应用工作流调度的算法具有更短的执行时间和费用,具有更高的效率、更好的优越性。

Research on workflow business hours-cost optimization problem.This paper proposed a novel dynamic particle swarm algorithm optimization method of workflow service subject.Through regional division,in each of the particles located in the region,when adapting to a value less than the best fitness,reinitializes region,so that the algorithm has better global convergence and dynamic adaptability,while the introduction of random disturbance,reverse operator,the search scope are expanded to the entire solution space in order to greatly improve the optimal solution probability.Combined with dynamic particle swarm algorithm based grid workflow scheduling problem of target model,and from three aspects of the cross time granularity,across time zones,the across working system,this paper discussed the workflow service subject selection method.The experimental results show that this method than other applications of grid workflow scheduling algorithm has shorter execution time and cost,higher efficiency,better superiority.