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Numerical Study of the Environmental and Economic System through the Computational Heuristic Based on Artificial

Kashif Nisar1, Zulqurnain Sabir2, Muhammad Asif Zahoor Raja3

  • 1Faculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu Sabah 88400, Malaysia.

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This study introduces an artificial neural network (ANN) combined with genetic algorithm (GA) and interior-point algorithm (IPA) for optimizing environmental and economic systems. The ANN-GA-IPA model effectively addresses complex nonlinear differential equations governing these systems.

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artificial neural networksenvironmental and economic systeminterior-pointnonlinear modelstatistical studies

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Area of Science:

  • Environmental Science
  • Computational Economics
  • Artificial Intelligence

Background:

  • Environmental and economic systems are complex and influenced by factors like control costs, emergency expenses, and industrial efficiency.
  • These interconnected factors create a nonlinear differential system that is challenging to model and optimize.
  • Existing methods may not fully capture the dynamic interplay between environmental regulations and economic performance.

Purpose of the Study:

  • To develop and evaluate a novel computational heuristic for optimizing environmental and economic systems.
  • To integrate artificial neural networks (ANNs) with genetic algorithms (GA) and interior-point algorithms (IPA) for enhanced system analysis.
  • To optimize an error-based objective function within a nonlinear differential environmental and economic system framework.

Main Methods:

  • Utilized a hybrid approach combining artificial neural networks (ANNs) for system structure, genetic algorithm (GA) for global search, and interior-point algorithm (IPA) for local search (ANN-GA-IPA).
  • Modeled the environmental and economic system as a nonlinear differential equation incorporating execution cost of control standards, elimination costs of emergencies, and industrial element competence.
  • Performed optimization of an error-based objective function using the defined differential system and its initial conditions.

Main Results:

  • The ANN-GA-IPA heuristic demonstrated capability in numerically computing and optimizing the complex environmental and economic system.
  • Successful optimization of the error-based objective function was achieved, indicating the model's effectiveness.
  • The study validates the integration of ANNs, GA, and IPA for tackling nonlinear differential systems in environmental and economic contexts.

Conclusions:

  • The developed ANN-GA-IPA heuristic provides an effective computational tool for optimizing environmental and economic systems.
  • This integrated approach offers a robust method for managing the complexities of environmental costs and economic performance.
  • Further research can explore refining the model for specific industrial applications and policy-making scenarios.