Task scheduling is a critical issue in distributed computing environments like cloud and fog.
The objective is to provide an optimal distribution of tasks among the resources. Several …

Expensive optimization problem (EOP) widely exists in various significant real-world
applications. However, EOP requires expensive or even unaffordable costs for evaluating …

Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

Unmanned aerial vehicle (UAV) has been extensively studied and widely adopted in
practical systems owing to its effectiveness and flexibility. Although heterogeneous UAVs …

As an advanced artificial intelligence technique for solving learning problems, deep learning
(DL) has achieved great success in many real-world applications and attracted increasing …

Cloud-fog computing frameworks are emerging paradigms developed to add benefits to the
current Internet of Things (IoT) architectures. In such frameworks, task scheduling plays a …

With the enormous increase in energy usage by cloud data centers for handling various
workflow applications, the energy-aware cloud workflow scheduling has become a hot …

As the population in cities continues to increase, large-city problems, including traffic
congestion and environmental pollution, have become increasingly serious. The …

In recent years, a variety of meta-heuristic nature-inspired algorithms have been proposed to
solve complex optimization problems. However, these algorithms suffer from the …

Grey wolf optimizer (GWO) is a widespread metaphor-based algorithm based on the
enhanced variants of velocity-free particle swarm optimizer with proven defects and …