An improved form of the ant lion optimization algorithm for image clustering problems

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dc.authorid
dc.contributor.authorToz, Metin
dc.date.accessioned2021-12-01T18:23:46Z
dc.date.available2021-12-01T18:23:46Z
dc.date.issued2019
dc.department[Belirlenecek]en_US
dc.description.abstractThis paper proposes an improved form of the ant lion optimization algorithm (IALO) to solve image clusteringproblem. The improvement of the algorithm was made using a new boundary decreasing procedure. Moreover, a recentlyproposed objective function for image clustering in the literature was also improved to obtain well-separated clusters while minimizing the intracluster distances. In order to accurately demonstrate the performances of the proposed methods, firstly, twenty-three benchmark functions were solved with IALO and the results were compared with the ALO and a chaos-based ALO algorithm from the literature. Secondly, four benchmark images were clustered by IALO and the obtained results were compared with the results of particle swarm optimization, artificial bee colony, genetic, and Kmeans algorithms. Lastly, IALO, ALO, and the chaos-based ALO algorithm were compared in terms of image clustering by using the proposed objective function for three benchmark images. The comparison was made for the objective function values, the separateness and compactness properties of the clusters and also for two clustering indexes Davies– Bouldin and Xie–Beni. The results showed that the proposed boundary decreasing procedure increased the performance of the IALO algorithm, and also the IALO algorithm with the proposed objective function obtained very competitive results in terms of image clustering.en_US
dc.identifier.doi10.3906/elk-1703-240
dc.identifier.endpage1460en_US
dc.identifier.issn1300-0632
dc.identifier.issn1300-0632
dc.identifier.issue2en_US
dc.identifier.startpage1445en_US
dc.identifier.urihttps://doi.org/10.3906/elk-1703-240
dc.identifier.urihttps://app.trdizin.gov.tr/makale/TXpNMk9EQXdNQT09
dc.identifier.urihttps://hdl.handle.net/20.500.12684/9770
dc.identifier.volume27en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorToz, Metin
dc.language.isoenen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBilgisayar Bilimleri, Yapay Zekaen_US
dc.subjectBilgisayar Bilimleri, Sibernitiken_US
dc.subjectBilgisayar Bilimleri, Donanım ve Mimarien_US
dc.subjectBilgisayar Bilimleri, Bilgi Sistemlerien_US
dc.subjectBilgisayar Bilimleri, Yazılım Mühendisliğien_US
dc.subjectBilgisayar Bilimleri, Teori ve Metotlaren_US
dc.subjectMühendislik, Elektrik ve Elektroniken_US
dc.titleAn improved form of the ant lion optimization algorithm for image clustering problemsen_US
dc.typeArticleen_US

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