Optimization of a Novel Urban Growth Simulation Model Integrating an Artificial Fish Swarm Algorithm and Cellular Automata for a Smart City

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