Calibrating Urban Expansion Cellular Automata Using Biogeography-Based Optimization

A new method is presented in this paper using biogeography-based optimization to calibrate urban expansion cellular automata (CA). Determining the transition rules and corresponding parameters is the key to a CA model. Biogeography-based optimization (BBO), is a new intelligent bionic optimization algorithm, solving problems by simulating the distribution, migration, and extinction of biological species. In this paper, a BBO algorithm is used to obtain transition rules and parameter values, and construct a BBO-CA model to simulate urban expansion. Compared with particle swarm optimization (PSO), the ant colony algorithm (ACO), genetic algorithm (GA), and logistic regression (LR), the BBO algorithm can effectively and quickly yield optimal and reasonable parameters. BBO performs effectively in terms of convergence and stability, with greater accuracy for urban cells and visual spatial layouts of simulation results. This paper illustrates the novel capabilities of the BBO algorithm for acquisition of variable parameters for urban cellular automata and has potential for simulations of other urban geographic phenomena.