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2024 | OriginalPaper | Chapter

Task Scheduling with Improved Particle Swarm Optimization in Cloud Data Center

Authors : Yang Bi, Wenlong Ni, Yao Liu, Lingyue Lai, Xinyu Zhou

Published in: Neural Information Processing

Publisher: Springer Nature Singapore

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Abstract

This paper proposes an improved particle swarm optimization algorithm with simulated annealing (IPSO-SA) for the task scheduling problem of cloud data center. The algorithm uses Tent chaotic mapping to make the initial population more evenly distributed. Second, a non-convex function is constructed to adaptively and decreasingly change the inertia weights to adjust the optimization-seeking ability of the particles in different iteration periods. Finally, the Metropolis criterion in SA is used to generate perturbed particles, combined with an modified equation for updating particles to avoid premature particle convergence. Comparative experimental results show that the IPSO-SA algorithm improves 13.8% in convergence accuracy over the standard PSO algorithm. The respective improvements over the other two modified PSO are 15.2% and 9.1%.

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Title: Task Scheduling with Improved Particle Swarm Optimization in Cloud Data Center
Authors: Yang Bi
Wenlong Ni
Yao Liu
Lingyue Lai
Xinyu Zhou
Publisher: Springer Nature Singapore
Book: Neural Information Processing
Print ISBN: 978-981-9980-66-6

Electronic ISBN: 978-981-9980-67-3

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-99-8067-3_21

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