Optimizing Settlements for Seasonal Agricultural Workers with Genetic Algorithms: A Case Study in Turkey

Abstract With the development of technology and industrialization, workers without agricultural land in rural areas migrate seasonally to other regions where they experience temporary housing problems. This study proposes a multi-objective settlement model for seasonal agricultural workers that can adapt to changing population and spatial requirements. The housing problem of seasonal agricultural workers is considered a multi-objective optimization problem, and it is transformed into numerical data. Using the NSGA-II genetic algorithm, optimization objectives such as the number of shelters, facility distribution, accessibility, and wind direction are defined, and a parametric model is developed using Grasshopper. Wallacei X was used for the NSGA-II algorithm. Among the 1,000 solutions obtained, 70 Pareto-optimal solutions were analyzed. The novelty of the model is that it provides feasible and rapidly optimized settlement plans for different population densities and seasons. This system offers flexible and sustainable solutions for various settlement areas.