Temporal Multi-Objective Optimization for Sustainable Agricultural Finance: Evidence from Evolutionary Algorithms

This study presents a modeling framework for multi-objective optimization in agricultural finance, emphasizing profitability, risk management, and sustainability. The proposed Advanced Financial Framework for Temporal Synergistic Optimization (AFFTSO) does not introduce a new algorithm; rather, it structures existing optimization workflows to explicitly integrate temporal dynamics, evolving objectives, feedback loops, and sustainability-oriented considerations. AFFTSO is designed to support long-term planning under fluctuating economic and environmental conditions. To demonstrate its applicability, AFFTSO is applied to a 25-year Turkish agricultural dataset (2000–2025), encompassing production, financial, market, and climate indicators. Two widely used evolutionary algorithms—Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)—are benchmarked within this framework, optimizing profit, financial risk, and resource-use efficiency simultaneously. Results show that NSGA-II consistently outperforms MOPSO, yielding a 12.4% increase in cumulative net profit, a 20.3% reduction in financial risk, and a 15.7% improvement in resource-use efficiency. These outcomes confirm that embedding temporal structures, adaptive objectives, and sustainability considerations into multi-objective optimization models enhances the robustness and resilience of financial planning. Overall, AFFTSO offers a practical approach for guiding resource allocation, investment planning, and risk-aware decision-making in agriculture. By bridging computational optimization with sustainability-oriented financial strategies, this framework supports the development of resilient agricultural systems that align economic performance with environmental and social objectives.