Automatic Parameterization for Expeditious Modelling of Virtual Urban Environments - A New Hybrid Metaheuristic

Expeditious modelling of virtual urban environments consists of generating realistic 3d models from limited information. It has several practical applications but typically suffers from a lack of accuracy in the parameter values that feed the modeller. By gathering small amounts of information about certain key urban areas, it becomes possible to feed a system that automatically compares and adjusts the input parameter values of modelling rules to find optimal parameter combinations. These optimum parameterized rules can then be reapplied in the generation of virtual urban models of areas with missing data but similar characteristics to the referenced area. Based on several nature inspired meta heuristic algorithms such as genetic algorithms, simulated annealing and harmony search, this paper presents a new hybrid meta heuristic algorithm capable of optimizing functions with both discrete and continuous parameters and offer competitive results in a highly neglected field of application.