Application of multi-objective genetic algorithm for optimal combination of resources to achieve sustainable...

Understanding the systemic approach and its potential for decision-making is important for resource management, especially in agriculture in which increasing food demands and environmental and social issues are the main challenges. Therefore, multiple-criteria decision-making methods have a vital role in the optimum combination of resources. Computational models are commonly used to assist resource management decision-making; however, while water-energy-food nexus (WEFN) are increasingly well modeled, the inclusion of social issues has lagged behind. This paper outlines a model based on a multi-objective genetic algorithm (MOGA) that conceptualizes and proceduralizes balancing the goals of sustainable agricultural development highlighting impacts and interactions between social variables and the WEFN index in agriculture. The model was developed using a bottom-up approach, informed through farmer interviews, and secondary data in the Miandarband plain, west Iran. The Compromise Programming (CP) method, which is widely used to solve MOGA models, was applied to optimization algorithms in three-dimensional spaces. The model represents field conditions and provides a tool for policymakers and sustainable resource management. The modeling framework applied to the study area for the comparison of WFEN, life cycle assessment (LCA), and social dimension in current and optimum cultivation patterns. The proposed optimal cultivation pattern in minimum CP will reduce water and energy consumption by 2.56 % and 12.71 % while reducing environmental impacts by 6.82 %, and it will improve the social status of farmers. Results suggest that changes in the basic elements of objective functions will lead to a balance between cultivation patterns that depends on policies and socio-economic conditions. Moreover, proposed cultivation patterns may be sustainable but their viability varies across the periods and also in different human ecologies. However, by analyzing the feedback of the model and interactions between different dimensions, this work highlights that policymakers can decide sustainable agriculture how should be occur by comparing different solutions.