Recurrent Neural Network Training using ABC Algorithm For Traffic Volume Prediction

Abstract

This study evaluates the use of the Artificial Bee Colony (ABC) algorithm to optimize the Recurrent Neural Network (RNN) that is used to analyze traffic volume. Related studies have shown that Deep Neural Networks are superseding the Shallow Neural Networks especially in terms of performance. Here we show that using the ABC algorithm in training the Recurrent Neural Network yields better results, compared to several other algorithms that are based on statistical or heuristic techniques that were preferred in earlier studies. The ABC algorithm is an example of swarm intelligence algorithms which are inspired by nature. Therefore, this study evaluates the performance of the RNN trained using the ABC algorithm for the purpose of forecasting. The performance metric used in this study is the Mean Squared Error (MSE) and ultimately, the outcome of the study may be generalized and extended to suit other domains.Povzetek: Ocena uspešnosti algoritma umetne kolonije čebelje pri optimizaciji ponavljajoče se nevronske mreže.

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“…Hence, an optimized LSTM with ABC to forecast the bitcoin price was introduced in Yuliyono and Girsang. 22 The combination of ABC and RNN was also proposed in Bosire 23 for traffic volume forecasting. This time the results were compared with standard backpropagation models.…”

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“…Hence, and optimized LSTM with ABC to forecast the bitcoin price was introduced in [21]. The combination of ABC and RNN was also proposed in [2] for traffic volume forecasting. This time the results were compared to standard backpropagation models.…”