论文标题
对基于人群的优化算法的比较评估，用于云雾环境中的工作流程计划
A Comparative Evaluation of Population-based Optimization Algorithms for Workflow Scheduling in Cloud-Fog Environments
论文作者
论文摘要
这项工作介绍了对云雾环境中工作流程计划的四种基于人群的优化算法的比较评估。这些算法如下：粒子群优化（PSO），遗传算法（GA），差异进化（DE）和GA-PSO。这项工作还为工作流程调度问题的加权总和目标函数提供了动机基础，并基于三个目标开发此功能：Makepan，成本和能源。最近提出的FogWorkflowsim用作上述目标的仿真环境。结果表明，GA-PSO算法的混合组合表现出比标准算法更好的。未来的工作将包括扩展通过增加任务数量以及添加更多工作流程的工作流程。还将追求加权目标功能的更多目标
This work presents a comparative evaluation of four population-based optimization algorithms for workflow scheduling in cloud-fog environments. These algorithms are as follows: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and GA-PSO. This work also provides the motivational groundwork for the weighted sum objective function for the workflow scheduling problem and develops this function based on three objectives: makespan, cost and energy. The recently proposed FogWorkflowSim is used as the simulation environment with the aforementioned objectives serving performance metrics. Results show that hybrid combination of the GA-PSO algorithm exhibits slightly better than the standard algorithms. Future work will include expansion of the workflows used by increasing the number of tasks as well as adding some more workflows. The addition of some more objectives to the weighted objective function will also be pursued
