| Autor: |
Al-Qaness MAA; College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China., Ewees AA; Department of Computer, Damietta University, Damietta 34517, Egypt., Abualigah L; Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19328, Jordan.; Faculty of Information Technology, Middle East University, Amman 11831, Jordan.; Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan.; School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia., AlRassas AM; School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China., Thanh HV; Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City 700000, Vietnam.; Faculty of Mechanical-Electrical and Computer Engineering, Van Lang University, Ho Chi Minh City 700000, Vietnam., Abd Elaziz M; Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt.; Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt.; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates.; Department of Electrical and Computer Engineering, Lebanese American University, Byblos 4307, Lebanon. |
| Abstrakt: |
The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine-cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods. |
