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Cited 23 time in webofscience Cited 28 time in scopus
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Harmony search algorithm for energy system applications: an updated review and analysis

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dc.contributor.authorNazari-Heris, Morteza-
dc.contributor.authorMohammadi-Ivatloo, Behnam-
dc.contributor.authorAsadi, Somayeh-
dc.contributor.authorKim, Jin-Hong-
dc.contributor.authorGeem, Zong Woo-
dc.date.available2020-02-27T02:22:15Z-
dc.date.created2020-02-04-
dc.date.issued2019-09-03-
dc.identifier.issn0952-813X-
dc.identifier.urihttps://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/999-
dc.description.abstractRecent advancements in energy systems have led to a series of new challenges in the decision-making process. Harmony search (HS) algorithm, which is a music-inspired optimisation technique, has been applied to some of these decision-making processes to obtain optimal set points within these energy systems. HS is based on the music improvisation process where musicians try to find better harmonies. Some of the advantages of HS method are that it is relatively simple to implement and require less algorithmic parameters. This paper aims to provide a comprehensive review on the applications of HS method to energy systems, that concentrate on two main objectives. First, the improved versions of HS introduced in recent studies will be reported. Second, contributed researches in energy systems by using HS will be analysed.-
dc.language영어-
dc.language.isoen-
dc.publisherTAYLOR & FRANCIS LTD-
dc.relation.isPartOfJOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE-
dc.subjectAUTOMATIC-GENERATION CONTROL-
dc.subjectLOAD FREQUENCY CONTROL-
dc.subjectOPTIMAL POWER-FLOW-
dc.subjectECONOMIC-ENVIRONMENTAL-DISPATCH-
dc.subjectPHASOR MEASUREMENT UNITS-
dc.subjectRENEWABLE MICRO-GRIDS-
dc.subjectOPTIMIZATION ALGORITHM-
dc.subjectSIZE OPTIMIZATION-
dc.subjectMULTIOBJECTIVE OPTIMIZATION-
dc.subjectSCHEDULING PROBLEM-
dc.titleHarmony search algorithm for energy system applications: an updated review and analysis-
dc.typeArticle-
dc.type.rimsART-
dc.description.journalClass1-
dc.identifier.wosid000486092000003-
dc.identifier.doi10.1080/0952813X.2018.1550814-
dc.identifier.bibliographicCitationJOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, v.31, no.5, pp.723 - 749-
dc.identifier.scopusid2-s2.0-85059449863-
dc.citation.endPage749-
dc.citation.startPage723-
dc.citation.titleJOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE-
dc.citation.volume31-
dc.citation.number5-
dc.contributor.affiliatedAuthorGeem, Zong Woo-
dc.type.docTypeReview-
dc.subject.keywordAuthorHarmony search-
dc.subject.keywordAuthorenergy systems-
dc.subject.keywordAuthoroptimisation-
dc.subject.keywordAuthormetaheuristics-
dc.subject.keywordPlusAUTOMATIC-GENERATION CONTROL-
dc.subject.keywordPlusLOAD FREQUENCY CONTROL-
dc.subject.keywordPlusOPTIMAL POWER-FLOW-
dc.subject.keywordPlusECONOMIC-ENVIRONMENTAL-DISPATCH-
dc.subject.keywordPlusPHASOR MEASUREMENT UNITS-
dc.subject.keywordPlusRENEWABLE MICRO-GRIDS-
dc.subject.keywordPlusOPTIMIZATION ALGORITHM-
dc.subject.keywordPlusSIZE OPTIMIZATION-
dc.subject.keywordPlusMULTIOBJECTIVE OPTIMIZATION-
dc.subject.keywordPlusSCHEDULING PROBLEM-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
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