A novel and effective optimization algorithm for global optimization and its engineering applications: Turbulent Flow of Water-based Optimization (TFWO)

Ghasemi, M; Davoudkhani, IF; Akbari, E; Rahimnejad, A; Ghavidel, S; Li, L

A novel and effective optimization algorithm for global optimization and its engineering applications: Turbulent Flow of Water-based Optimization (TFWO)

Ghasemi, M Davoudkhani, IF Akbari, E Rahimnejad, A Ghavidel, S Li, L

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Engineering Applications of Artificial Intelligence, 2020, 92, pp. 103666-103666
Issue Date:: 2020-06-01

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Field	Value	Language
dc.contributor.author	Ghasemi, M
dc.contributor.author	Davoudkhani, IF
dc.contributor.author	Akbari, E
dc.contributor.author	Rahimnejad, A
dc.contributor.author	Ghavidel, S
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.date.accessioned	2020-10-18T19:43:06Z
dc.date.available	2020-10-18T19:43:06Z
dc.date.issued	2020-06-01
dc.identifier.citation	Engineering Applications of Artificial Intelligence, 2020, 92, pp. 103666-103666
dc.identifier.issn	0952-1976
dc.identifier.uri	http://hdl.handle.net/10453/143345
dc.description.abstract	© 2020 Elsevier Ltd In this study we present a new and effective grouping optimization algorithm (namely, the Turbulent Flow of Water-based Optimization (TFWO)), inspired from a nature search phenomenon, i.e. whirlpools created in turbulent flow of water, for global real-world optimization problems. In the proposed algorithm, the problem of selecting control parameters is eliminated, the convergence power is increased and the algorithm have a fixed structure. The proposed algorithm is used to find the global solutions of real-parameter benchmark functions with different dimensions. Besides, in order to further investigate the effectiveness of TFWO, it was used to solve various types of nonlinear Economic Load Dispatch (ELD) optimization problems in power systems and Reliability–RedundancyAllocation Optimization (RRAO) for the overspeed protection system of a gas turbine, as two real-world engineering optimization problems. The results of TFWO are compared with other algorithms, which provide evidence for efficient performance with superior solution quality of the proposed TFWO algorithm in solving a great range of real-parameter benchmark and real-world engineering problems. Also, the results prove the competitive performance and robustness of TFWO algorithm compared to other state-of-the-art optimization algorithms in this study. The source codes of the TFWO algorithm are publicly available at https://github.com/ebrahimakbary/TFWO.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Engineering Applications of Artificial Intelligence
dc.relation.isbasedon	10.1016/j.engappai.2020.103666
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A novel and effective optimization algorithm for global optimization and its engineering applications: Turbulent Flow of Water-based Optimization (TFWO)
dc.type	Journal Article
utslib.citation.volume	92
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-18T19:43:02Z
pubs.publication-status	Published
pubs.volume	92

Abstract:

© 2020 Elsevier Ltd In this study we present a new and effective grouping optimization algorithm (namely, the Turbulent Flow of Water-based Optimization (TFWO)), inspired from a nature search phenomenon, i.e. whirlpools created in turbulent flow of water, for global real-world optimization problems. In the proposed algorithm, the problem of selecting control parameters is eliminated, the convergence power is increased and the algorithm have a fixed structure. The proposed algorithm is used to find the global solutions of real-parameter benchmark functions with different dimensions. Besides, in order to further investigate the effectiveness of TFWO, it was used to solve various types of nonlinear Economic Load Dispatch (ELD) optimization problems in power systems and Reliability–RedundancyAllocation Optimization (RRAO) for the overspeed protection system of a gas turbine, as two real-world engineering optimization problems. The results of TFWO are compared with other algorithms, which provide evidence for efficient performance with superior solution quality of the proposed TFWO algorithm in solving a great range of real-parameter benchmark and real-world engineering problems. Also, the results prove the competitive performance and robustness of TFWO algorithm compared to other state-of-the-art optimization algorithms in this study. The source codes of the TFWO algorithm are publicly available at https://github.com/ebrahimakbary/TFWO.

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http://hdl.handle.net/10453/143345