计算机科学
工作流程
云计算
局部最优
调度(生产过程)
数学优化
早熟收敛
群体行为
作业车间调度
分布式计算
算法
粒子群优化
人工智能
地铁列车时刻表
数学
数据库
操作系统
作者
Huifang Li,Yizhu Wang,Jingwei Huang,Yushun Fan
标识
DOI:10.1016/j.jpdc.2022.02.005
摘要
Nowadays, many scientific applications are deployed in the cloud to execute at a lower cost. However, the growing scale of workflows makes scheduling problems challenging. To minimize the workflow execution cost under deadline constraints, this article proposes a Mutation and Dynamic Objective-based Farmland Fertility (MDO-FF) algorithm for obtaining a near-optimal solution within a relatively shorter time. A Dynamic Objective Strategy (DOS) is introduced to accelerate the convergence speed, while a multi-swarm evolutionary approach and mutation strategies are incorporated to enhance the search diversity and help to escape from local optima. By seeking new potential solutions and searching in its corresponding neighborhoods, our proposed MDO-FF can make a good trade-off between exploration and exploitation. Extensive experiments are conducted on well-known scientific workflows with different types and sizes. The experimental results demonstrate that in most cases, our MDO-FF outperforms the existing algorithms in terms of constraint satisfiability and solution quality.
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