Energy management of an intelligent parking lot equipped with hydrogen storage systems and renewable energy sources using the stochastic p-robust optimization approach

Habib, Salman; Aghakhani, Sina; Nejati, Mobin Ghasempour; Amir, Vahid; Jia, Youwei; Ahmed, Emad M.

doi:10.1016/j.energy.2023.127844

Cited by 12 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…11,12 Finding their way into many fields, optimization techniques are also showing promising results to better help scientists in the field of molecular simulations. 13–16…”

Section: Introductionmentioning

confidence: 99%

Motion of nanovehicles on pristine and vacancy-defected silicene: implications for controlled surface motion

Youzi,

Kianezhad,

Vaezi

et al. 2023

Phys. Chem. Chem. Phys.

View full text Add to dashboard Cite

show abstract

“…11,12 Finding their way into many fields, optimization techniques are also showing promising results to better help scientists in the field of molecular simulations. 13–16…”

Section: Introductionmentioning

confidence: 99%

Motion of nanovehicles on pristine and vacancy-defected silicene: implications for controlled surface motion

Youzi,

Kianezhad,

Vaezi

et al. 2023

Phys. Chem. Chem. Phys.

View full text Add to dashboard Cite

show abstract

“…By harnessing IoT data with machine learning, deep learning, and neural networks, workflow scheduling becomes more agile, responsive, and adept at handling the dynamic and complex demands of cloud-based applications, ultimately improving efficiency, resource utilization, and overall performance. Their integration not only streamlines operations but also paves the way for self-optimizing and self-adapting systems in cloud workflow management [19,20].…”

Section: Introductionmentioning

confidence: 99%

Efficient Cloud Workflow Scheduling with Inverted Ant Colony Optimization Algorithm

DING,

ZHANG

2023

IJACSA

View full text Add to dashboard Cite

Cloud computing has risen as a prominent paradigm, offering users on-demand access to computing resources and services via the Internet. In cloud environments, workflow scheduling plays a vital role in optimizing resource utilization, reducing execution time, and minimizing overall costs. As workflows comprise interdependent tasks that need to be assigned to Virtual Machines (VMs), the complexity of the scheduling problem increases in proportion to workflow size and VM availability. Due to its NP-hard nature, finding an optimal scheduling solution for workflows remains a challenging task. To address this problem, researchers have turned to metaheuristic approaches, which have shown promise in finding near-optimal solutions for complex combinatorial optimization problems. This paper proposes a novel metaheuristic algorithm called Inverted Ant Colony Optimization (IACO) for workflow scheduling in cloud environments. IACO is a variation of the traditional ACO algorithm, where the updated pheromone has an inverted influence on the path chosen by the ants. By leveraging the complementary nature of these two algorithms, our proposed algorithm aims to achieve superior workflow scheduling performance regarding total execution time and cost, surpassing existing approaches.

show abstract

Optimal Band Selection Using Evolutionary Machine Learning to Improve the Accuracy of Hyper-spectral Images Classification: a Novel Migration-Based Particle Swarm Optimization

Vahidi,

Aghakhani,

Martín

et al. 2023

J Classif

View full text Add to dashboard Cite

Energy management of an intelligent parking lot equipped with hydrogen storage systems and renewable energy sources using the stochastic p-robust optimization approach

Cited by 12 publications

References 36 publications

Motion of nanovehicles on pristine and vacancy-defected silicene: implications for controlled surface motion

Motion of nanovehicles on pristine and vacancy-defected silicene: implications for controlled surface motion

Efficient Cloud Workflow Scheduling with Inverted Ant Colony Optimization Algorithm

Optimal Band Selection Using Evolutionary Machine Learning to Improve the Accuracy of Hyper-spectral Images Classification: a Novel Migration-Based Particle Swarm Optimization

Contact Info

Product

Resources

About