Performance comparison of MOLA, IP, and GSA optimization algorithms in urban land use allocation based on landscape metrics

Sustainable land-use planning refers to the effort to establish a balance between economic growth, ecological structures, environmental protection, and social progress. Therefore, land-use suitability assessment and extract comprehensive objectives are essential. In recent years, the use of artificial intelligence (AI) tools significantly increased for land-use planning. In this study, the Multi-Objective Land Allocation (MOLA) algorithm, Gravitational Search Algorithm (GSA), and Image Processing (IP) technique have been applied to urban land use location of the Birjand watershed based on a comprehensive set of sustainable development goals. The objectives used include maximizing fitness functions (e.g., environmental and ecological suitability, compression functions, and landscape stability), minimizing land-use conversion, imposing limitations of flood protected areas with an above 70% slope, the demands of urban areas, placement one land use per pixel. Visual assessment, statistical and landscape metrics analysis were employed to compare algorithms' outcomes. results showed that the MOLA (with an average of 215.136) had better allocation concerning land use suitability assessment for urban development. Also, MOLA and IP algorithms (with standard deviations of 41.037 and 41.729, respectively) were placed in better positions than GSA. Additionally, landscape metrics analysis indicated that there were relative efficiency and superiority between different metrics of the studied algorithms.