Efficient task and workflow scheduling are very important for improving resource
management and reducing power consumption in cloud computing data centers (DCs) …

Workflow scheduling is a largely studied research topic in cloud computing, which targets to
utilize cloud resources for workflow tasks by considering the objectives specified in QoS. In …

Large-scale multiobjective optimization problems (LSMOPs) are characterized as
optimization problems involving hundreds or even thousands of decision variables and …

Fog and cloud computing are emerging paradigms that enable distributed and scalable data
processing and analysis. However, these paradigms also pose significant challenges for …

Many real-world multi-objective optimization problems are dynamic. These problems require
an optimization algorithm to quickly track optimal solutions after changing the environment …

Most of the existing dynamic multi-objective evolutionary algorithms (DMOEAs) are effective,
which focuses on searching for the approximation of Pareto-optimal front (POF) with well …

The characteristic of large-scale multiobjective optimization problems (LSMOPs) is
optimizing multiple conflicting objectives while considering thousands of decision variables …

Prediction methods are widely used to solve dynamic multi-objective optimization problems
(DMOPs). The key to the success of prediction methods lies in the accurate tracking of the …

Dynamic multi-objective optimization problems (DMOPs) have been of interest to many
researchers. These are problems in which the environment changes during the evolutionary …

Managing scientific applications in the Cloud poses many challenges in terms of workflow
scheduling, especially in handling multi-objective workflow scheduling under quality of …