Training of Multilayer Artificial Neural Network with Snow Ablation Optimizer Algorithm

Yıl 2024, Cilt: 39 Sayı: 2, 391 - 406, 11.07.2024

Feyza Nur Özdemir Ahmet Özkış

https://doi.org/10.21605/cukurovaumfd.1514409

Öz

The snow ablation optimizer (SAO) is a new metaheuristic algorithm proposed in 2023, inspired by snow melting. In this study, a hybrid model was developed with the SAO algorithm to update the artificial neural network weights. The developed hybrid model was compared with hybrid models created with gray wolf, reptile search, cuckoo and sine cosine algorithms on five different data sets named aggregation, balance, liver, pathbased and wine. Four different metrics called sensitivity, specificity, precision and f1-score were used to measure the success of the models. The success ranking of the models for each data set and the average success ranking for all data sets are given. When the results are examined, it is seen that the SAO model ranks 2nd in the wine data set and 1st in all other data sets for all metrics. Regarding average success rank, the SAO model achieved the best result with a value of 1.2 in all metrics. In addition, convergence graphs of the mean square error values of the hybrid models in the training phase were drawn and it was observed that the SAO hybrid model had a faster convergence performance than the compared models in all other data sets except Wine. Finally, the effect of the number of particles in the population on the success of the hybrid SAO model was analyzed and it was observed that the success increased when the number of individuals was 100.

Anahtar Kelimeler

Snow ablation optimizer algorithm, Artificial neural networks, Machine learning, Supervised learning, Classification

Kar Erime Optimizasyonu Algoritması ile Çok Katmanlı Yapay Sinir Ağının Eğitimi

Öz

Kar erime optimizasyonu (snow ablation optimizer, SAO) algoritması, karın erimesinden ilham alınarak 2023 yılında önerilen yeni bir metasezgisel algoritmadır. Bu çalışmada, yapay sinir ağının ağırlıklarının güncellenmesi amacıyla SAO algoritması ile hibrit bir model geliştirilmiştir. Geliştirilen hibrit model aggregation, balance, liver, pathbased ve wine adlı beş farklı veri seti üzerinde gri kurt, sürüngen arama, guguk kuşu ve sinüs kosinüs algoritmaları ile oluşturulan hibrit modeller ile karşılaştırılmıştır. Modellerin başarısını ölçmek için duyarlılık, özgüllük, kesinlik ve f1-puanı adı verilen dört farklı metrik kullanılmıştır. Modellerin her veri seti için başarı sıralaması ve tüm veri setleri için ortalama başarı sıralaması verilmiştir. Sonuçlar incelendiğinde, SAO modelinin tüm metrikler için wine veri setinde 2., diğer tüm veri setlerinde 1. olduğu görülmektedir. Ortalama başarı sırası açısından ise SAO modeli tüm metriklerde 1.2 değeri ile en iyi sonucu elde etmiştir. Ayrıca hibrit modellerin, eğitim aşamasındaki ortalama karesel hata değerlerinin yakınsama grafikleri çizdirilmiş ve SAO hibrit modelinin wine hariç diğer tüm veri setlerinde karşılaştırılan modellerden daha hızlı bir yakınsama performansına sahip olduğu gözlenmiştir. Son olarak popülasyondaki parçacık sayısının hibrit SAO modelinin başarısına etkisi analiz edilmiş ve birey sayısının 100 olması durumunda başarının arttığı gözlenmiştir.

Anahtar Kelimeler

Kar erime optimizasyonu algoritması, Yapay sinir ağları, Makine öğrenmesi, Danışmanlı öğrenme, Sınıflandırma

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