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Article: Application of quantum-inspired evolutionary algorithm in transmission network expansion planning

TitleApplication of quantum-inspired evolutionary algorithm in transmission network expansion planning
Authors
KeywordsPower System
Quantum-Inspired Evolutionary Algorithm
Transmission Network Expansion Planning
Issue Date2004
PublisherPower System Technology Press. The Journal's web site is located at http://www.dwjs.com.cn/
Citation
Power System Technology, 2004, v. 28 n. 17, p. 19-23 How to Cite?
AbstractQuantum-inspired evolutionary algorithm is a kind of evolutionary computation algorithm in the form of quantum chromosome and is based on the concept of quantum computation. The best individual of current ones is used for the next search, and the whole interference crossover operation is used to avoid pre-maturity, so it possesses rapid convergence and good global search ability. Transmission network expansion planning is a complicated, nonlinear, large-scale combinatorial optimization problem. Here, a model of power system transmission expansion planning based on quantum-inspired evolutionary algorithm (QEA) is presented. In this model the parameters in the algorithm are optimized and a more suitable fetch of rotation angle of quantum rotation gate to the research subject is put forward. The results of calculation examples show that the presented model is reliable and effective and its calculation accuracy and speed can be further improved by adding special operator.
Persistent Identifierhttp://hdl.handle.net/10722/155475
ISSN
2015 SCImago Journal Rankings: 0.684
References

 

DC FieldValueLanguage
dc.contributor.authorHou, YHen_US
dc.contributor.authorLu, LJen_US
dc.contributor.authorXiong, XYen_US
dc.contributor.authorWu, YWen_US
dc.date.accessioned2012-08-08T08:33:41Z-
dc.date.available2012-08-08T08:33:41Z-
dc.date.issued2004en_US
dc.identifier.citationPower System Technology, 2004, v. 28 n. 17, p. 19-23en_US
dc.identifier.issn1000-3673en_US
dc.identifier.urihttp://hdl.handle.net/10722/155475-
dc.description.abstractQuantum-inspired evolutionary algorithm is a kind of evolutionary computation algorithm in the form of quantum chromosome and is based on the concept of quantum computation. The best individual of current ones is used for the next search, and the whole interference crossover operation is used to avoid pre-maturity, so it possesses rapid convergence and good global search ability. Transmission network expansion planning is a complicated, nonlinear, large-scale combinatorial optimization problem. Here, a model of power system transmission expansion planning based on quantum-inspired evolutionary algorithm (QEA) is presented. In this model the parameters in the algorithm are optimized and a more suitable fetch of rotation angle of quantum rotation gate to the research subject is put forward. The results of calculation examples show that the presented model is reliable and effective and its calculation accuracy and speed can be further improved by adding special operator.en_US
dc.languageengen_US
dc.publisherPower System Technology Press. The Journal's web site is located at http://www.dwjs.com.cn/en_US
dc.relation.ispartofPower System Technologyen_US
dc.subjectPower Systemen_US
dc.subjectQuantum-Inspired Evolutionary Algorithmen_US
dc.subjectTransmission Network Expansion Planningen_US
dc.titleApplication of quantum-inspired evolutionary algorithm in transmission network expansion planningen_US
dc.typeArticleen_US
dc.identifier.emailHou, YH:yhhou@eee.hku.hken_US
dc.identifier.authorityHou, YH=rp00069en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-4544220745en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-4544220745&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume28en_US
dc.identifier.issue17en_US
dc.identifier.spage19en_US
dc.identifier.epage23en_US
dc.publisher.placeChinaen_US
dc.identifier.scopusauthoridHou, YH=7402198555en_US
dc.identifier.scopusauthoridLu, LJ=7403962870en_US
dc.identifier.scopusauthoridXiong, XY=7201634426en_US
dc.identifier.scopusauthoridWu, YW=7406898040en_US

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