As one of the 17 Sustainable Development Goals, it is sensible to analysis historical urban land use characteristics and project the potentials of urban sustainable development for a smart city. The cellular automaton (CA) model is the widely applied in simulating urban growth, but the optimum parameters of variables driving urban growth in the model remains to be continued to improve. We propose a novel model integrating an artificial fish swarm algorithm (AFSA) and CA for optimizing parameters of variables in the urban growth model and make a comparison between AFSA-CA and other five models, which is used to study a 40-year urban land growth of Wuhan. We found that the urban growth types from 1995 to 2015 appeared relatively consistent, mainly including infilling, edge-expansion and distant-leap types in Wuhan, which a certain range of urban land growth on the periphery of the central area. Additionally, although the genetic algorithms (GA)-CA model and the AFSA-CA model among the six models due to the distance variables, the parameter value of the GA-CA model is −15.5409 according to the fact that the population (POP) variable should be positively. As a result, the AFSA-CA model regardless of the initial parameter setting is superior to the GA-CA model and the GA-CA model is superior to all the other models. Finally, it is projected that the potentials of urban growth in Wuhan for 2025 and 2035 under three scenarios (natural urban land growth without any restrictions (NULG), sustainable urban land growth with cropland protection and ecological security (SULG), and economic urban land growth with sustainable development and economic development in the core area (EULG)) focus mainly on existing urban land and some new town centers based on AFSA-CA urban growth simulation model. An increasingly precise simulation can determine the potential increase area and quantity of urban land, providing a basis to judge the layout of urban land use for urban planners.
Optimization of a Novel Urban Growth Simulation Model Integrating an Artificial Fish Swarm Algorithm and Cellular Automata for a Smart City
