A Genetic Algorithm Enhancement of MOLA Approach Using Landscape Metrics

There is competition between land uses in spatial land use allocation. In this research two approaches including Multi Objective Land Allocation (MOLA) and optimization with Genetic Algorithm (GA) were used for conflict resolution. The MOLA approach is based on suitability and proximity to ideal point whereas in GA، a suitability layer together with a cohesion index was used for land use allocation. The output from MOLA application was fed into the GA approach as the initial population and contiguity index as a landscape metric was used in the process to improve the result. With inclusion of contiguity in the GA approach which is absent in MOLA، and has no precedence in Iran، the final patches in the land use pattern were compacter and better shaped. Results showed GA application using MOLA output improves landscape metrics specifications in the final land use plan. However، including landscape metrics compromises suitability for land use، but there is possibility of balancing suitability and landscape indices in the GA application process. The final layer created through GA showed capability of considering suitability and landscape metrics simultaneously in land use planning towards achieving an optimal solution.