Conventional raster-based cellular automata (CA) confront many difficulties because of cell
size and neighborhood sensitivity. Alternatively, vector CA-based models are very complex
and difficult to implement. We present a hybrid cellular automata (HCA) model as a
combination of cellular structure and vector concept. The space is still defined by a set of
cells, but rasterized spatial objects are also utilized in the structure of transition rules.
Particle swarm optimization (PSO) is also used to calculate the urbanization probability of …

This paper presents an improved cellular automata (CA) model of urban growth based on
particle swarm optimization (PSO) approach with inertia weight. An innovative feature of this
cellular model is the incorporation of swarm intelligence to stochastically optimize the
transition rules to reduce the simulation uncertainties and improve its locational accuracy in
urban modeling. The similarity between the nature of self-organization of particle swarm
optimizers and the bottom-up approach of cellular automata makes PSO particularly suitable …