:: Volume 5, Issue 1 (6-2017) ::
jgit 2017, 5(1): 49-64 Back to browse issues page
Agent-Based Modeling of Urban Growth With Communications Inspired by Particle Swarm Optimization Algoritm
Farnaz Kaviari *, Mohamad Sadi Mesgari, Farhad Hosseinali, Samane Vaezi
K.N.Toosi University of Technology
Abstract:   (3244 Views)

Population growth, urbanization and immigration from rural areas to cities results in increasing expansion of
the cities. The adequate urban development requires proper and accurate planning, such those facilities for the
new areas that are provided and environmental impacts are lessened. Computer based simulation and
prediction of urban growth can be assumed as a good start for urban planning. In this research, a new agent
base simulation of urban growth is developed, in which the communication and decision of agents are imitated
from the Particle Swarm Optimization (PSO) algorithm. The model is tested on the urban growth of Zanjan
city- Iran between 2005 and 2015. In this model, land developers are classified into three groups of agents
according to their income level. These agents search the environment and find proper lands for development
according to their priorities and conditions. The output of the model is 74% similar to the reality according to
the Kappa index. This and other results show that the model can predict the expansion of the city adequately.
Moreover, the comparison made shows that modeling of the relations and communications between agents
similar to PSO can slightly improve the quality of the model. The results showed the adequacy of the proposed
agent-based modelling for the simulation of urban growth. To have a more accurate model, it is recommended
to model the behavior of the agents with more details and also to consider the competition between agents.

Keywords: Urban Growth, Simulation, agent, PSO
Full-Text [PDF 1574 kb]   (1885 Downloads)    
Type of Study: Research | Subject: GIS
Received: 2017/06/10 | Accepted: 2017/06/10 | Published: 2017/06/10



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Volume 5, Issue 1 (6-2017) Back to browse issues page