Simulating multi-objective land use optimization allocation using Multi-agent system-A case study in Changsha, China

Achieving multi-objective land use optimization allocation (MOLUOA) for sustainable development is an important issue in land use. In consideration of the multi-dimensional characteristics of MOLUOA in terms of quantity, space, and time, and under the constraints of maximizing economic, ecological, and social benefits of land use, a MOLUOA model is developed in this study by integrating multi-agent system with particle swarm optimization. The MOLUOA model is applied to the simulation of land use optimization allocation in Changsha, a typical city located in central China. Simulation results show that the MOLUOA model can achieve multi-objective land use optimization allocation in terms of quantity, space, and time. The model can provide decision-making support for generating land use alternatives to achieve sustainable land use.