Aquila Optimizer Based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing

Abualigah, L; Elaziz, MA; Khodadadi, N; Forestiero, A; Jia, H; Gandomi, AH

Aquila Optimizer Based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing

Abualigah, L Elaziz, MA Khodadadi, N Forestiero, A Jia, H Gandomi, AH

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Publisher:: Springer International Publishing
Publication Type:: Journal Article
Citation:: 2022, 1038, pp. 481-497
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Abualigah, L
dc.contributor.author	Elaziz, MA
dc.contributor.author	Khodadadi, N
dc.contributor.author	Forestiero, A
dc.contributor.author	Jia, H
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2023-02-10T09:00:19Z
dc.date.available	2023-02-10T09:00:19Z
dc.date.issued	2022-01-01
dc.identifier.citation	2022, 1038, pp. 481-497
dc.identifier.isbn	9783030990787
dc.identifier.issn	1860-949X
dc.identifier.issn	1860-9503
dc.identifier.uri	http://hdl.handle.net/10453/166053
dc.description.abstract	This paper introduces IAO, a new swarm intelligence approach for addressing the challenge of task scheduling in cloud computing. The proposed method uses conventional Aquila Optimizer (AO) and Particle Swarm Optimizer (PSO) as a hybrid method based on a novel transition mechanism. The proposed hybrid method, IAO, combined the AO and PSO to avoid the weaknesses they face; these weaknesses are trapped in the local search area and have low solution diversity. The proposed transition mechanism is proposed to acquire proper changes between the search operators in order to keep the improvements; it changes between them when any algorithm gets stuck or the solutions diversity decreases. Several scenarios are conducted and tested to validate the suggested method’s ability to address the task scheduling problem; these scenarios contain various tasks (i.e., 600, 1000, and 2000). The obtained results are compared with other well-known methods in terms of Max, Mean, Min of the Expected Complete Time (ECT), Friedman ranking test, and Wilcoxon signed-rank test. The proposed IAO method achieved better results and promising compared to other comparative methods; it is an excellent scheduling approach for solving any related scheduling problem.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.isbasedon	10.1007/978-3-030-99079-4_19
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Aquila Optimizer Based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing
dc.type	Journal Article
utslib.citation.volume	1038
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2023-02-10T09:00:18Z
pubs.publication-status	Published
pubs.volume	1038

Abstract:

This paper introduces IAO, a new swarm intelligence approach for addressing the challenge of task scheduling in cloud computing. The proposed method uses conventional Aquila Optimizer (AO) and Particle Swarm Optimizer (PSO) as a hybrid method based on a novel transition mechanism. The proposed hybrid method, IAO, combined the AO and PSO to avoid the weaknesses they face; these weaknesses are trapped in the local search area and have low solution diversity. The proposed transition mechanism is proposed to acquire proper changes between the search operators in order to keep the improvements; it changes between them when any algorithm gets stuck or the solutions diversity decreases. Several scenarios are conducted and tested to validate the suggested method’s ability to address the task scheduling problem; these scenarios contain various tasks (i.e., 600, 1000, and 2000). The obtained results are compared with other well-known methods in terms of Max, Mean, Min of the Expected Complete Time (ECT), Friedman ranking test, and Wilcoxon signed-rank test. The proposed IAO method achieved better results and promising compared to other comparative methods; it is an excellent scheduling approach for solving any related scheduling problem.

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http://hdl.handle.net/10453/166053