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A hybrid multi-objective artificial bee colony algorithm for flexible task scheduling problems in cloud computing system

  • Jun-qing LiEmail author
  • Yun-qi Han
Article
  • 43 Downloads

Abstract

In this study, the flexible task scheduling problem in a cloud computing system is studied and solved by a hybrid discrete artificial bee colony (ABC) algorithm, where the considered problem is firstly modeled as a hybrid flowshop scheduling (HFS) problem. Both a single objective and multiple objectives are considered. In multiple objective HFS problems, three objectives, i.e., minimization of the maximum completion time, maximum device workload, and total workloads of all devices, are considered simultaneously. Two different kinds of HFS are considered, i.e., HFS with identical parallel machines and HFS with unrelated machines. In the proposed algorithm, three types of artificial bees are included as in the classical ABC algorithm, i.e., the employed bee, the onlooker bee, and the scout bee. Each solution is represented as an integer string. To consider the problem features, several different types of perturbation structures are investigated to enhance the searching abilities. An improved version of the adaptive perturbation structure is embedded in the proposed algorithm to balance the exploitation and exploration ability. A simple but efficient selection and updated approach are applied to enhance the exploitation process. To further improve the exploitation abilities, a deep-exploitation operator is designed. An improved scout bee employed with different local search methods for the best food source or the abandoned solution is designed and can increase the convergence ability of the proposed algorithm. The proposed algorithm is tested on sets of the well-known benchmark instances, and the performance of the proposed algorithm is verified.

Keywords

Hybrid flowshop scheduling problem Artificial bee colony algorithm Cloud system Flexible task scheduling 

Notes

Acknowledgements

This research is partially supported by National Science Foundation of China under Grants 61773192, 61773246 and 61803192, Shandong Province Higher Educational Science and Technology Program (J17KZ005), Special fund plan for local science and technology development lead by central authority, major basic research projects in Shandong (ZR2018ZB0419), and also under Grant of Key Laboratory of Intelligent Optimization and Control with Big Data.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer ScienceLiaocheng UniversityLiaochengPeople’s Republic of China
  2. 2.School of Information Science and EngineeringShandong Normal UniversityJinanPeople’s Republic of China

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