Intelligent Decision Optimization for Resource-Efficient and Resilient Agriculture-Food Systems: A Hybrid Fuzzy Evolutionary Framework: An Economic Perspective

Sustainable management of agri-food systems requires advanced decision-support tools capable of integrating and balancing trade-offs across economic, food-security, nutritional, and food-system dimensions under uncertainty. This study develops a hybrid Mamdani Fuzzy Inference System–Multi-Objective Genetic Algorithm (MFIS–MOGA) framework using a harmonized global panel dataset covering the 2000–2025 analytical period, subject to indicator-specific data availability. Data from FAOSTAT, the World Bank, FAO food-security and nutrition databases, the FAO Healthy Diet Cost Database, and United Nations repositories were harmonized, preprocessed, normalized, and incorporated into MATLAB R2024a. The MFIS transformed heterogeneous indicators into interpretable sustainability assessments, while the MOGA explored trade-offs among Economic Performance, Food Security, Nutritional Sustainability, Food Loss and Waste, and the integrated Sustainability Index. The first non-dominated front contained 42 Rank-1 solutions, while five representative solutions were reported for comparative interpretation. Across the complete Rank-1 Pareto set, the Sustainability Index ranged from 0.742 to 0.770. Agreement between the model-generated and reference sustainability-score series yielded R2 = 0.964, RMSE = 0.0348, MAE = 0.0261, and MAPE = 4.87%; these statistics are interpreted as internal agreement measures rather than independent predictive validation because the reference series is derived from the same underlying indicator structure. The results demonstrate the capacity of the MFIS–MOGA framework to represent multidimensional sustainability trade-offs while maintaining interpretability and computational transparency. The sensitivity analysis identified GDP per capita as the most influential factor, followed by the Crop Production Index, Food Production Index, and Agricultural Value Added. This novel approach provides a flexible and clear workflow underpinned by evidence for the development of informed agricultural policies.