Performance Analysis of Source-Linked Harmony Search Algorithm for Big Data Optimization

Yıl 2022, Cilt: 15 Sayı: 2, 151 - 160, 15.12.2022

Serhat Celil İleri Selçuk Aslan Sercan Demirci

https://doi.org/10.54525/tbbmd.1090787

Öz

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.

Anahtar Kelimeler

Harmony Search Algorithm, Source-linked Harmony Search Algorithm, Big Data Optimisation

Büyük Veri Optimizasyonu için Kaynak-Bağlantılı Harmoni Arama Algoritmasının Performans Analizi

Öz

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.

Anahtar Kelimeler

Harmoni Arama Algoritması, Kaynak-Bağlantılı HSA, Büyük Veri Optimizasyonu, EEG, Harmony Search Algorithm, Source-linked Harmony Search Algorithm, Big Data Optimisation

Kaynakça

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