In this study, the performance of Source-Linked Harmony Search algorithm (slinkHSA) that is a new variant of the Harmony Search algorithm (HSA) powered with the usage of existing data for generating initial solutions was investigated over a big data optimization problem requiring minimization of measurement noise for electroencephalography (EEG) signals. The results obtained by the mentioned HSA variant were also compared to the results of other meta-heuristic techniques. Comparative studies showed that generating initial harmonies by guiding the existing EEG signals significantly contributes to the qualities of the solutions and increases the convergence speed of the algorithm.
Bu çalışmada, Harmoni Arama algoritmasının (Harmony Search algorithm, HSA) mevcut veriden faydalanarak başlangıç çözümlerini üretme yaklaşımı ile güçlendirilmiş varyantı olan Kaynak-Bağlantılı Harmoni Arama algoritmasının (Source-Linked HSA, slinkHSA) performansı elektroensefalografi (EEG) sinyallerinde gürültü minimizasyonu gerektiren büyük veri optimizasyonu üzerinden incelenmiştir. slinkHSA ile elde edilen sonuçlar diğer meta-sezgisel teknikler tarafından bulunan sonuçlar üzerinden kıyaslanmıştır. Karşılaştırmalar, başlangıç harmonilerini EEG sinyalleri kullanılarak üretmenin çözümlerinin kalitesini önemli ölçüde katkıda bulunduğunu ve algoritmanın yakınsama hızını artırdığını göstermiştir.
Harmoni Arama Algoritması Kaynak-Bağlantılı HSA Büyük Veri Optimizasyonu EEG Harmony Search Algorithm Source-linked Harmony Search Algorithm Big Data Optimisation
Primary Language | Turkish |
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Subjects | Engineering |
Journal Section | Makaleler(Araştırma) |
Authors | |
Early Pub Date | December 3, 2022 |
Publication Date | December 15, 2022 |
Published in Issue | Year 2022 Volume: 15 Issue: 2 |
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