Optimized selection of suitable sites for farmland consolidation projects using multi-objective genetic algorithms

In order to select suitable sites for farmland consolidation projects, correlation analysis and evolutionary algorithms were used to optimize the evaluation of ecological, social and economic factors, avoiding subjective selection and ignorance of spatial relationships among land attributes. Multi-objective Genetic Algorithms (MOGA) were applied to select the best sites from the perspective of spatial relationship and land attribute evaluation. With carefully defined restrictions and variables, multi-objective optimization is able to select several suitable sites for farmland consolidation projects. The results from a case study in Yangshan, Guangdong of China showed that the selected sites were on the central and southern Yangshan with expected flat terrain and abundant water resources. An empirical experiment also demonstrated that the proposed method is able to provide well selected sites for land consolidation projects.