Firefly algorithm-based cellular automata for reproducing urban growth and predicting future scenarios

Optimization of cellular automata (CA) using swarm intelligence is an effective approach to establish optimal models for urban growth modeling. This study builds a new CA model (CA(FFA)) using the firefly algorithm through optimizing transition rules, aiming to enhance the simulation accuracy. We applied CA(FFA) to reproduce historical urban growth (2009-2014 and 2014-2019) and simulate future scenarios (2024 and 2029) of the Xi-xian metropolitan area in 2009, 2014 and 2019. The retrieved CA(FFA) parameters and land transition probability maps indicate that the traffic facilities have more significant impacts on urban growth in Xi-xian than the socioeconomic and other proximity factors. The overall accuracy of CA(FFA) is 90.2% in 2014 and 94.6% in 2019, both better than the logistic CA model (88.9% and 92.2%). The figure-of-merits of CA(FFA) in calibration and validation are respectively increased by 4.84% and 8.10% compared with the logistic CA model. With the increasing distance to the city center, new urban areas will first increase and then decrease, and be mainly distributed in the northern part, which are consistent with the local development planning and have strong practical significance. The proposed model can improve CA-based modeling methods and enhance our understanding of Chinese western cities.