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Evolutionary Intelligence
Published in: Evolutionary Intelligence 4/2021

11-09-2020 | Research Paper

Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing

Authors: Ali Mohammadzadeh, Mohammad Masdari, Farhad Soleimanian Gharehchopogh, Ahmad Jafarian

Published in: Evolutionary Intelligence | Issue 4/2021

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Abstract

The workflow scheduling in the cloud computing environment is a well-known NP-complete problem, and metaheuristic algorithms are successfully adapted to solve this problem more efficiently. Grey wolf optimization (GWO) is a recently proposed interesting metaheuristic algorithm to deal with continuous optimization problems. In this paper, we proposed IGWO, an improved version of the GWO algorithm which uses the hill-climbing method and chaos theory to achieve better results. The proposed algorithm can increase the convergence speed of the GWO and prevents falling into the local optimum. Afterward, a binary version of the proposed IGWO algorithm, using various S functions and V functions, is introduced to deal with the workflow scheduling problem in cloud computing data centers, aiming to minimize their executions’ cost, makespan, and the power consumption. The proposed workflow scheduling scheme is simulated using the CloudSim simulator and the results show that our scheme can outperform other scheduling approaches in terms of metrics such as power consumption, cost, and makespan.
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Footnotes
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Metadata
Title
Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing
Authors
Ali Mohammadzadeh
Mohammad Masdari
Farhad Soleimanian Gharehchopogh
Ahmad Jafarian
Publication date
11-09-2020
Publisher
Springer Berlin Heidelberg
Published in
Evolutionary Intelligence / Issue 4/2021
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-020-00479-5

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