A bio-inspired crime simulation model
In this paper we describe a multiagent crime simulation model that resorts to concepts of self-organizing bio-inspired systems, in particular, of the Ant Colony Optimization algorithm. As the matching between simulated and real crime data distributions depends upon the tuning of some control parameters of the simulation model (in particular, of the initial places where criminals start out), we have modeled the calibration of the simulation as an optimization problem. The solution for the allocation of criminals into gateways is also undertaken by a bio-inspired method, namely, a customized Genetic Algorithm. We show that this approach allows for the automatic discovery of gateway configurations that, when employed in the simulation, produce crime distributions that are statistically close to those observed in real data.
crime simulation bio-inspired systems ant colony optimization genetic algorithms social networks multiagent simulation