Adaptive modified artificial bee colony algorithms (AMABC) for optimization of complex systems

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dc.authorscopusid57203171979
dc.authorscopusid14053392600
dc.contributor.authorKorkmaz Tan, Rabia
dc.contributor.authorBora, Şebnem
dc.date.accessioned2022-05-11T14:03:00Z
dc.date.available2022-05-11T14:03:00Z
dc.date.issued2020
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractComplex systems are large scale and involve numerous uncertainties, which means that such systems tend to be expensive to operate. Further, it is difficult to analyze systems of this kind in a real environment, and for this reason agent-based modeling and simulation techniques are used instead. Based on estimation methods, modeling and simulation techniques establish an output set against the existing input set. However, as the data set in a given complex systems becomes very large, it becomes impossible to use estimation methods to create the output set desired. Therefore, a new mechanism is needed to optimize data sets in this context. In this paper, the adaptive modified artificial bee colony algorithm is shown to be successful in optimizing the numerical test function and complex system parameter data sets. Moreover, the results show that this algorithm can be successfully adapted to a given problem. Specifically, this algorithm can be more successful in optimizing problem solving than either the artificial bee colony algorithm or the modified artificial bee colony algorithm. The adaptive modified artificial bee colony algorithm performs a search in response to feedback received from the simulation in run-time. Because of its adaptability, the adaptive modified artificial bee colony algorithm is of great importance for its ability to find solutions to multiple kinds of problems across numerous fields. © TÜBİTAK
dc.identifier.doi10.3906/ELK-1909-12
dc.identifier.endpage2629
dc.identifier.issn1300-0632
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85095797600
dc.identifier.scopusqualityQ3
dc.identifier.startpage2602
dc.identifier.urihttps://doi.org/10.3906/ELK-1909-12
dc.identifier.urihttps://hdl.handle.net/20.500.11776/4568
dc.identifier.volume28
dc.identifier.wosWOS:000576687400001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKorkmaz Tan, Rabia
dc.language.isoen
dc.publisherTurkiye Klinikleri
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAdaptive modified artificial bee colony
dc.subjectArtificial bee colony
dc.subjectComplex system
dc.subjectModified artificial bee colony
dc.subjectOptimization
dc.subjectParameter setting
dc.subjectAutonomous agents
dc.subjectComputational methods
dc.subjectAgent-based modeling and simulation
dc.subjectArtificial bee colony algorithms
dc.subjectEstimation methods
dc.subjectModeling and simulation techniques
dc.subjectNew mechanisms
dc.subjectNumerical tests
dc.subjectParameter data
dc.subjectReal environments
dc.subjectOptimization
dc.titleAdaptive modified artificial bee colony algorithms (AMABC) for optimization of complex systems
dc.typeArticle

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