An Example of Classification Using a Neural Network Trained by the Zebra Optimization Algorithm

Abstract

Artificial intelligence techniques are a broad field of research with training, computation and prediction capabilities. Among these techniques, artificial neural networks (ANNs) are widely used as a predictive model. Learning algorithms in ANN classifiers have great importance on the success of ANN. The ANN model generally uses gradient-based learning models. However, due to the disadvantages of gradient-based learning models in local search, they have begun to be replaced by heuristic-based algorithms in recent years. Heuristic algorithms have attracted the attention of many researchers in recent years due to their success in problem solving. In this study, the Zebra Optimization Algorithm (ZOA), which has been proposed recently to train ANN networks, was examined. The main purpose of this study is to train the neural network using ZOA and increase the sensitivity of the perceptron neural network. In this study, a new ANN network integrated with ZOA is proposed. In this study, a detailed parameter analysis was carried out to show the effect of the population size and maximum generation number parameter settings, which form the basis for ZOA, on the ANN network. Then, a parameter analysis was carried out for the number of layers, number of neurons and epoch values, which are important for ANN networks. Such an ideal ANN network has been identified. This ideal ANN model was run on seven different data sets and was successful in predicting accurate data. In addition, three different heuristic algorithms (Gazelle Optimization Algorithm (GOA), Prairie Dogs Optimization (PDO), and Osprey Optimization Algorithm (OOA)) selected from the literature were integrated on the same ANN model and compared with the results of ANN integrated with ZOA operated under similar conditions. The results reveal that the proposed algorithm leads to greater convergence with the neural network coefficient compared to other algorithms. In addition, the proposed method caused the prediction error in the neural network to decrease.

Keywords

• ANN
• Zebra
• Layer
• Neuron
• Network
• Prediction

Ethical Statement

The study does not require ethics committee permission or any special permission.

Thanks

The Zebra Optimization Algorithm (ZOA) code used in this study is available on MATLAB Central File Exchange: Zebra Optimization Algorithm (ZOA). Furthermore, the datasets for instances referenced in this study can be accessed at https://archive.ics.uci.edu/datasets. We would like to express our gratitude to these sources for providing valuable data for our research.

Zebra Optimizasyon Algoritması Tarafından Eğitilmiş Bir Sinir Ağının Kullanıldığı Sınıflandırma Örneği

Abstract

Yapay zeka teknikleri eğitim, hesaplama ve tahmin yeteneklerine sahip geniş bir araştırma alanıdır. Bu teknikler arasında yapay sinir ağları (YSA) tahmin modeli olarak yaygın olarak kullanılmaktadır. YSA sınıflandırıcılarındaki öğrenme algoritmaları YSA'nın başarısı üzerinde büyük önem taşımaktadır. YSA modeli genellikle gradyan tabanlı öğrenme modellerini kullanır. Ancak yerel aramada gradyan tabanlı öğrenme modellerinin dezavantajları nedeniyle son yıllarda yerini sezgisel tabanlı algoritmalar almaya başlamıştır. Sezgisel algoritmalar problem çözmedeki başarılarından dolayı son yıllarda birçok araştırmacının dikkatini çekmiştir. Bu çalışmada YSA ağlarının eğitimi için son dönemde önerilen Zebra Optimizasyon Algoritması (ZOA) incelenmiştir. Bu çalışmanın temel amacı sinir ağını ZOA kullanarak eğitmek ve algılayıcı sinir ağının duyarlılığını arttırmaktır. Bu çalışmada ZOA ile entegre yeni bir YSA ağı önerilmektedir. Bu çalışmada ZOA'ya temel oluşturan popülasyon büyüklüğü ve maksimum nesil sayısı parametre ayarlarının YSA ağı üzerindeki etkisini göstermek amacıyla detaylı bir parametre analizi yapılmıştır. Daha sonra YSA ağları için önemli olan katman sayısı, nöron sayısı ve çağ değerleri için parametre analizi yapılmıştır. Böyle ideal bir YSA ağı belirlendi. Bu ideal YSA modeli yedi farklı veri seti üzerinde çalıştırılmış ve doğru verileri tahmin etmede başarılı olmuştur. Ayrıca literatürden seçilen üç farklı sezgisel algoritma (Ceylan Optimizasyon Algoritması (GOA), Çayır Köpekleri Optimizasyonu (PDO), and Balıkkartalı Optimizasyon Algoritması (OOA)) aynı YSA modeli üzerine entegre edilmiş ve benzer koşullar altında çalışan ZOA ile entegre edilmiş YSA'nın sonuçları ile karşılaştırılmıştır. Sonuçlar, önerilen algoritmanın diğer algoritmalara göre sinir ağı katsayısı ile daha fazla yakınsamaya yol açtığını ortaya koymaktadır. Ayrıca önerilen yöntem sinir ağındaki tahmin hatasının azalmasına neden olmuştur.

Keywords

• ANN
• Zebra
• Katman
• Nöron
• Ağ
• Tahmin

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