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Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for

Mohammed A A Al-Qaness1, Ahmed A Ewees2, Laith Abualigah3,4,5,6

  • 1College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China.

Entropy (Basel, Switzerland)
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Summary

Optimizing crude oil production forecasts using artificial neural networks (ANNs) is crucial for economic planning. This study enhances the dendritic neural regression (DNR) model with metaheuristic optimization algorithms, finding PSO and WOA most effective for time-series prediction.

Keywords:
dendritic neural regression (DNR)forecastingmetaheuristicoil productionparticle swarm optimizationtime-series

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Area of Science:

  • Petroleum Engineering
  • Artificial Intelligence
  • Computational Science

Background:

  • Accurate crude oil forecasting is vital for national economic planning.
  • Artificial Neural Networks (ANNs), particularly the Dendritic Neural Regression (DNR) model, show potential for time-series prediction due to their ability to handle nonlinear data.
  • The DNR model's training and parameter configuration present limitations that hinder its optimal performance.

Purpose of the Study:

  • To enhance the performance of the Dendritic Neural Regression (DNR) model for crude oil time-series forecasting.
  • To investigate the efficacy of various metaheuristic (MH) optimization algorithms in optimizing DNR model parameters.
  • To compare the performance of six MH algorithms: Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Sine-Cosine Algorithm (SCA), Differential Evolution (DE), and Harmony Search (HS).

Main Methods:

  • The study employed six metaheuristic optimization algorithms (WOA, PSO, GA, SCA, DE, HS) to optimize the parameters of the Dendritic Neural Regression (DNR) model.
  • Crude oil production data from seven real-world oilfields in Tahe, China, were utilized for historical time-series analysis.
  • Performance was evaluated using multiple metrics to assess the effectiveness of MH-optimized DNR models in time-series forecasting applications.

Main Results:

  • The integration of metaheuristic optimization algorithms significantly improved the performance of the original DNR model.
  • All tested MH algorithms demonstrated applicability in enhancing DNR model training and parameter optimization.
  • The Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) algorithms exhibited superior performance compared to the other evaluated MH methods.

Conclusions:

  • Metaheuristic optimization techniques are effective in improving the performance of the Dendritic Neural Regression model for crude oil production forecasting.
  • The PSO and WOA algorithms provide the most robust and accurate results for optimizing DNR models in this context.
  • This research confirms the value of combining advanced optimization algorithms with ANNs for reliable crude oil time-series prediction.