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Article: Transmission network expansion planning based on estimation of distribution algorithms

TitleTransmission network expansion planning based on estimation of distribution algorithms
Authors
KeywordsEstimation Of Distribution Algorithm (Eda)
Factorized Distribution Algorithm
Population-Based Incremental Learning
Power System
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. 23, p. 32-37 How to Cite?
AbstractEstimation of distribution algorithms (EDA) is a new kind of evolution algorithm, through the statistics of the information of selected individual in current group the probability estimation of the individual distribution in next generation is given, and the next generation of group is formed by random sampling. Here, the EDA is applied to solve the transmission network expansion planning problem, two models of transmission network expansion planning based on EDA, i.e., the population-based incremental learning: (PBIL) and factorized distribution algorithm (EDA), are put forward. The influence of several strategies, such as weighted estimation, random size of parents set selection, readjusting of the conditional probability sequence, stochastic mutation and elitist reserved on the algorithm is analyzed. Simulation results show that the used EDA is reliable and effective for solving the transmission network expansion planning problems.
Persistent Identifierhttp://hdl.handle.net/10722/155244
ISSN
2015 SCImago Journal Rankings: 0.684
References

 

DC FieldValueLanguage
dc.contributor.authorHou, YHen_US
dc.contributor.authorZheng, FLen_US
dc.contributor.authorLu, LJen_US
dc.contributor.authorXiong, XYen_US
dc.date.accessioned2012-08-08T08:32:31Z-
dc.date.available2012-08-08T08:32:31Z-
dc.date.issued2004en_US
dc.identifier.citationPower System Technology, 2004, v. 28 n. 23, p. 32-37en_US
dc.identifier.issn1000-3673en_US
dc.identifier.urihttp://hdl.handle.net/10722/155244-
dc.description.abstractEstimation of distribution algorithms (EDA) is a new kind of evolution algorithm, through the statistics of the information of selected individual in current group the probability estimation of the individual distribution in next generation is given, and the next generation of group is formed by random sampling. Here, the EDA is applied to solve the transmission network expansion planning problem, two models of transmission network expansion planning based on EDA, i.e., the population-based incremental learning: (PBIL) and factorized distribution algorithm (EDA), are put forward. The influence of several strategies, such as weighted estimation, random size of parents set selection, readjusting of the conditional probability sequence, stochastic mutation and elitist reserved on the algorithm is analyzed. Simulation results show that the used EDA is reliable and effective for solving the transmission network expansion planning problems.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.subjectEstimation Of Distribution Algorithm (Eda)en_US
dc.subjectFactorized Distribution Algorithmen_US
dc.subjectPopulation-Based Incremental Learningen_US
dc.subjectPower Systemen_US
dc.subjectTransmission Network Expansion Planningen_US
dc.titleTransmission network expansion planning based on estimation of distribution algorithmsen_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-s2.0-10844249489en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-10844249489&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume28en_US
dc.identifier.issue23en_US
dc.identifier.spage32en_US
dc.identifier.epage37en_US
dc.publisher.placeChinaen_US
dc.identifier.scopusauthoridHou, YH=7402198555en_US
dc.identifier.scopusauthoridZheng, FL=11339240000en_US
dc.identifier.scopusauthoridLu, LJ=7403962870en_US
dc.identifier.scopusauthoridXiong, XY=7201634426en_US

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