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An efficient meta-heuristic algorithm for grid computing

Author

Listed:
  • Zahra Pooranian

    (Dezful Islamic Azad University)

  • Mohammad Shojafar

    (Sapienza University of Rome)

  • Jemal H. Abawajy

    (Deakin University)

  • Ajith Abraham

    (Scientific Network for Innovation and Research Excellence)

Abstract

A grid computing system consists of a group of programs and resources that are spread across machines in the grid. A grid system has a dynamic environment and decentralized distributed resources, so it is important to provide efficient scheduling for applications. Task scheduling is an NP-hard problem and deterministic algorithms are inadequate and heuristic algorithms such as particle swarm optimization (PSO) are needed to solve the problem. PSO is a simple parallel algorithm that can be applied in different ways to resolve optimization problems. PSO searches the problem space globally and needs to be combined with other methods to search locally as well. In this paper, we propose a hybrid-scheduling algorithm to solve the independent task-scheduling problem in grid computing. We have combined PSO with the gravitational emulation local search (GELS) algorithm to form a new method, PSO–GELS. Our experimental results demonstrate the effectiveness of PSO–GELS compared to other algorithms.

Suggested Citation

  • Zahra Pooranian & Mohammad Shojafar & Jemal H. Abawajy & Ajith Abraham, 2015. "An efficient meta-heuristic algorithm for grid computing," Journal of Combinatorial Optimization, Springer, vol. 30(3), pages 413-434, October.
    • Handle: RePEc:spr:jcomop:v:30:y:2015:i:3:d:10.1007_s10878-013-9644-6
    DOI: 10.1007/s10878-013-9644-6
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    References listed on IDEAS

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    1. J.E. Orosz & S.H. Jacobson, 2002. "Analysis of Static Simulated Annealing Algorithms," Journal of Optimization Theory and Applications, Springer, vol. 115(1), pages 165-182, October.
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    Citations

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    Cited by:

    1. Xin Zhang & Dexuan Zou & Xin Shen, 2018. "A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 6(12), pages 1-34, November.
    2. Xin Yang & Zhenxiang Zeng & Ruidong Wang & Xueshan Sun, 2016. "Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-13, December.
    3. Mohit Agarwal & Gur Mauj Saran Srivastava, 2018. "Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1237-1267, July.
    4. Wanting Zhang & Ming Zeng & Peng Guo & Kun Wen, 2022. "Variable Neighborhood Search for Multi-Cycle Medical Waste Recycling Vehicle Routing Problem with Time Windows," IJERPH, MDPI, vol. 19(19), pages 1-25, October.
    5. Adinarayanan Arunagiri & Uthayakumar Marimuthu & Prabhakaran Gopalakrishnan & Adam Slota & Jerzy Zajac & Maheandera Prabu Paulraj, 2017. "Sustainability Formation of Machine Cells in Group Technology Systems Using Modified Artificial Bee Colony Algorithm," Sustainability, MDPI, vol. 10(1), pages 1-21, December.

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