A GA based job scheduling strategy for computational grid

Singh, Krishan Veer; Raza, Zahid

doi:10.1109/icacea.2015.7164708

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Different scheduling plans generate different cost expenditures, so we need to model them as a multi-objective optimization problem, while considering task completion time and cost expenditures. The workflow task scheduling problem itself is an NP-hard problem [1], which cannot find the optimal solution in an effective time. When we shift our goals from single to multiple, the problem becomes more complex.…”

Section: Introductionmentioning

confidence: 99%

Ant colony optimization-based multi-objective task scheduling algorithm for supercomputing internet workflow

Fu,

Wang,

Guo

2024

Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023)

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Section: Introductionmentioning

confidence: 99%

Ant colony optimization-based multi-objective task scheduling algorithm for supercomputing internet workflow

Fu,

Wang,

Guo

2024

Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023)

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“…Evolutionary algorithms fall into the category of metaheuristics algorithms and are widely used for problems such as NP-hard combinatorial problems [2], job scheduling problems [3], and multi-objective optimization problems [4]- [10]. Meta-heuristic algorithms have two main directions: intensification and diversification.…”

Section: Introductionmentioning

confidence: 99%

Raccoon Optimization Algorithm

et al. 2019

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Population-based meta-heuristic is a high-level method intended to provide sufficient solution for problems with incomplete information among a massive volume of solutions. However, it does not guarantee to attain global optimum in a reasonable time. To improve the time and accuracy of the coverage in the population-based meta-heuristic, this paper presents a novel algorithm called the raccoon optimization algorithm (ROA). The ROA is inspired by the rummaging behaviors of real raccoons for food. Raccoons are successful animals because of their extraordinarily sensitive and dexterous paws and their ability to find solutions for foods and remember them for up to three years. These capabilities make raccoons expert problem solvers and allow them to purposefully seek optimum solutions. These behaviors exploited in the ROA to search the solution spaces of nonlinear continuous problems to find the global optimum with higher accuracy and lower time coverage. To evaluate the ROA's ability in addressing complicated problems, it has been tested on several benchmark functions. The ROA is then compared with nine other wellknown optimization algorithms. These experiments show that the ROA achieves higher accuracy with lower coverage time. INDEX TERMS Raccoon optimization algorithm (ROA), nonlinear continuous optimization problems, structural optimization, evolutionary algorithm, meta-heuristic algorithm. NOMENCLATURE

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Task Allocation in Distributed Real Time Database Systems in IoT

Choudhary

Jha

2020

Advances in Intelligent Systems and Computing

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A GA based job scheduling strategy for computational grid

Cited by 5 publications

References 17 publications

Ant colony optimization-based multi-objective task scheduling algorithm for supercomputing internet workflow

Ant colony optimization-based multi-objective task scheduling algorithm for supercomputing internet workflow

Raccoon Optimization Algorithm

Task Allocation in Distributed Real Time Database Systems in IoT

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