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

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.