A hybrid Jaya algorithm for reliability–redundancy allocation problems

Ghavidel, S; Azizivahed, A; Li, L

A hybrid Jaya algorithm for reliability–redundancy allocation problems

Ghavidel, S Azizivahed, A Li, L

Permalink

Publication Type:: Journal Article
Citation:: Engineering Optimization, 2018, 50 (4), pp. 698 - 715
Issue Date:: 2018-04-03

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted ManuscriptAdobe PDF (838.01 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Ghavidel, S	en_US
dc.contributor.author	Azizivahed, A	en_US
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216	en_US
dc.date.available	2020-05-25T19:10:27Z
dc.date.issued	2018-04-03	en_US
dc.identifier.citation	Engineering Optimization, 2018, 50 (4), pp. 698 - 715	en_US
dc.identifier.issn	0305-215X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/110515
dc.description.abstract	© 2017 Informa UK Limited, trading as Taylor & Francis Group. This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching–learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability–redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series–parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30–100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results.	en_US
dc.relation.ispartof	Engineering Optimization	en_US
dc.relation.isbasedon	10.1080/0305215X.2017.1337755	en_US
dc.subject.classification	Operations Research	en_US
dc.title	A hybrid Jaya algorithm for reliability–redundancy allocation problems	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	50	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
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/Students
utslib.copyright.status	open_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	50	en_US

Abstract:

© 2017 Informa UK Limited, trading as Taylor & Francis Group. This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching–learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability–redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series–parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30–100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/110515