Objective Description
This analysis employs the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), a robust multi-objective evolutionary algorithm, to address complex optimization problems. The study includes two scientifically relevant case studies:
1. Car Example: Optimizes fuel consumption and maximum speed based on vehicle weight and power. This is critical in automotive engineering for designing sustainable vehicles, balancing environmental impact (fuel efficiency) with performance (speed), which influences market competitiveness and regulatory compliance.
2. DRASTIC Index Example: Optimizes weights of the DRASTIC parameters (Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and Conductivity) to maximize correlation with nitrate concentration (NO3) while minimizing Root Mean Square Error (RMSE). This enhances groundwater vulnerability assessment, providing valuable insights for environmental management and policy-making in regions prone to contamination.
The analysis generates Pareto fronts to visualize trade-offs, computes optimal solutions, and exports results as CSV files for further scientific evaluation.
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