Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/808
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kaya, Ersin | - |
dc.contributor.author | Uymaz, Sait Ali | - |
dc.contributor.author | Koçer, Barış | - |
dc.date.accessioned | 2021-12-13T10:30:01Z | - |
dc.date.available | 2021-12-13T10:30:01Z | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 1868-8071 | - |
dc.identifier.issn | 1868-808X | - |
dc.identifier.uri | https://doi.org/10.1007/s13042-018-0878-6 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/808 | - |
dc.description.abstract | Galactic swarm optimization (GSO) is a new global metaheuristic optimization algorithm. It manages multiple sub-populations to explore search space efficiently. Then superswarm is recruited from the best-found solutions. Actually, GSO is a framework. In this framework, search method in both sub-population and superswarm can be selected differently. In the original work, particle swarm optimization is used as the search method in both phases. In this work, performance of the state of the art and well known methods are tested under GSO framework. Experiments show that performance of artificial bee colony algorithm under the GSO framework is the best among the other algorithms both under GSO framework and original algorithms. | en_US |
dc.language.iso | en | en_US |
dc.publisher | SPRINGER HEIDELBERG | en_US |
dc.relation.ispartof | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Galactic Swarm Optimization | en_US |
dc.subject | Artificial Bee Colony Algorithm | en_US |
dc.subject | Swarm Intelligence | en_US |
dc.subject | Metaheuristic Optimization Algorithm | en_US |
dc.subject | Bee Colony Algorithm | en_US |
dc.subject | Differential Evolution Algorithm | en_US |
dc.subject | Artificial Algae Algorithm | en_US |
dc.subject | Particle Swarm | en_US |
dc.subject | Global Optimization | en_US |
dc.subject | Intelligence | en_US |
dc.title | Boosting galactic swarm optimization with ABC | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1007/s13042-018-0878-6 | - |
dc.identifier.scopus | 2-s2.0-85070677925 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.authorid | KAYA, Ersin/0000-0001-5668-5078 | - |
dc.authorwosid | UYMAZ, Sait Ali/ABA-7308-2020 | - |
dc.authorwosid | KAYA, Ersin/V-7558-2019 | - |
dc.identifier.volume | 10 | en_US |
dc.identifier.issue | 9 | en_US |
dc.identifier.startpage | 2401 | en_US |
dc.identifier.endpage | 2419 | en_US |
dc.identifier.wos | WOS:000481418600013 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 36348487700 | - |
dc.authorscopusid | 56572779600 | - |
dc.authorscopusid | 35786168500 | - |
dc.identifier.scopusquality | Q1 | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | embargo_20300101 | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.03. Department of Computer Engineering | - |
crisitem.author.dept | 02.03. Department of Computer Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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File | Size | Format | |
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s13042-018-0878-6.pdf Until 2030-01-01 | 2.65 MB | Adobe PDF | View/Open Request a copy |
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