Surrogate-assisted Population Based ACO for Resource Constrained Job Scheduling with Uncertainty

2022 ◽  
pp. 101029
Author(s):  
Dhananjay Thiruvady ◽  
Su Nguyen ◽  
Fatemeh Shiri ◽  
Nayyar Zaidi ◽  
Xiaodong Li
Keyword(s):  
Job Scheduling ◽  
Population Based ◽  
Resource Constrained

Resource Constrained Job Scheduling with Parallel Constraint-Based ACO

2016 ◽  
pp. 266-278 ◽  
Author(s):  
Dror Cohen ◽  
Antonio Gómez-Iglesias ◽  
Dhananjay Thiruvady ◽  
Andreas T. Ernst
Keyword(s):  
Job Scheduling ◽  
Resource Constrained

Parallel ant colony optimization for resource constrained job scheduling

2014 ◽  
Vol 242 (2) ◽  
pp. 355-372 ◽  
Author(s):  
Dhananjay Thiruvady ◽  
Andreas T. Ernst ◽  
Gaurav Singh
Keyword(s):  
Ant Colony Optimization ◽  
Job Scheduling ◽  
Ant Colony ◽  
Resource Constrained

Resource-constrained job scheduling with recyclable resources

1995 ◽  
Vol 81 (2) ◽  
pp. 364-375 ◽  
Author(s):  
John P. Shewchuk ◽  
T.C. Chang
Keyword(s):  
Job Scheduling ◽  
Resource Constrained ◽  
Recyclable Resources

A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job Scheduling in Computational Grid

2018 ◽  
Vol 9 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Tarun Kumar Ghosh ◽  
Sanjoy Das
Keyword(s):  
Differential Evolution ◽  
Firefly Algorithm ◽  
Job Scheduling ◽  
Population Based ◽  
Computational Grids ◽  
Hybrid Population ◽  
Shared Resources ◽  
De Algorithm ◽  
Processing Cost ◽  
Comparison Results

Scheduling jobs in computational Grids is considered as NP-complete problem owing to the heterogeneity of shared resources. The resources belong to many distributed administrative domains that enforce various management policies. Therefore, the use of meta-heuristics are more appropriate option in obtaining optimal results. In this article, a novel hybrid population-based global optimization algorithm, called the Hybrid Firefly Algorithm and the Differential Evolution (HFA-DE), is proposed by combining the merits of both the Firefly Algorithm and Differential Evolution. The Firefly Algorithm and the Differential Evolution are executed in parallel to support information sharing amongst the population and thus enhance searching efficiency. The proposed HFA-DE algorithm reduces the schedule makespan, processing cost, and improves resource utilization. The HFA-DE is compared with the standard Firefly Algorithm, the Differential Evolution and the Particle Swarm Optimization algorithms on all these parameters. The comparison results exhibit that the proposed algorithm outperforms the other three algorithms.

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