Parçacık Sürüsü Optimizasyonu ile Optimize Edilmiş Yapay Sinir Ağı Modeli Kullanılarak Turizm Gelirlerinin Tahmini

Abstract

Yapay sinir ağı (YSA), son yıllarda tahmin çalışmalarında yaygın olarak kullanılan makine öğrenme algoritmalarından biridir. YSA ile etkili tahmin sonuçları elde etmenin anahtarı, ağın eğitimi ve ayarlanabilir parametrelerinin tasarımına bağlıdır. Geri yayılım ve Levenberg-Marquardt (BP-LM) öğrenme algoritmaları, YSA'nın eğitimi için en çok kullanılan algoritmalardır. Ancak, bu algoritmalar yerel minimumlara takılma, hesaplama karmaşıklığı, başlangıç değerlerine duyarlılık, aşırı öğrenme ve sınırlı paralelleştirme gibi bazı dezavantajlara sahiptir. Bu çalışmada, YSA öğreniminde karşılaşılan bu sorunları ele almak amacıyla Parçacık Sürüsü Optimizasyonu (PSO) ile eğitilmiş bir YSA modeli önerilmiştir. PSO, en iyi çözüme ulaşabilme, az sayıda ayarlanabilir parametreye sahip olma, kolay paralelleştirme, sağlamlık ve hızlı yakınsama özellikleri sayesinde öğrenme sürecinde başarılı olabilen yapay zeka algoritmalarından biridir. Önerilen model, turizm geliri tahmini için farklı YSA yapıları ve parametre değerleri ile test edilmiştir. Sonuç olarak, önerilen PSO ile eğitilmiş YSA modelinin genel olarak BP-LM ile eğitilmiş YSA'ya kıyasla daha iyi tahmin sonuçları verdiği gözlemlenmiş ve turizm gelirlerinin tahmini için en uygun YSA yapısı belirlenmiştir. Ayrıca, belirlenen optimal YSA yapısı ile önümüzdeki 12 ay için turizm gelir tahmini gerçekleştirilmiştir.

Keywords

• Yapay Zeka
• Yapay Sinir Ağları
• Makine Öğrenmesi
• Parçacık Sürü Optimizasyonu

An Optimized Artificial Neural Network Model with Particle Swarm Optimization for Tourism Revenue Prediction

Abstract

Artificial neural network (ANN) is one of the machine learning algorithms widely used in prediction studies recently. The key to obtaining effective prediction results with ANN depends on its training and the design of its tunable parameters. The Backpropagation and Levenberg-Marquardt (BP-LM) learning algorithms are the most utilized algorithms for training ANN. However, these algorithms have some disadvantages such as local minima, computational complexity, sensitivity to initialization, overfitting, and limited parallelism. In this study, we proposed a Particle Swarm Optimization (PSO)-trained ANN model to deal with these problems in ANN learning. PSO is one of the well-utilized artificial intelligence algorithms and it can be successful in the learning process thanks to its features of finding global optima, having a few parameters to be tuned, being easily parallelized, robustness and convergence speed. The proposed model is tested with different ANN structures and parameter values for tourism revenue prediction. As a result, it was observed that the proposed PSO-trained ANN model generally gave better prediction results than BP-LM trained ANN and an optimal ANN structure was obtained for the prediction of tourism revenues. In addition, forecasting of tourism revenues for the next 12 months was obtained with a designed optimal ANN structure.

Keywords

• Artificial intelligence
• Artificial Neural Network
• Machine Learning
• Particle Swarm Optimization
• Prediction of Tourism Revenues.

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