A parallelized genetic algorithm for the calibration of Lowry model
This paper presents a parallelized genetic algorithm for the calibration of Lowry model based on a maximum likelihood approach. A case study for the city of Hong Kong was employed for demonstrating the performance of the parallelized genetic algorithm, in terms of two commonly used performance measures: speedup and efficiency. The genetic algorithm is particularly suitable for implementation under a parallel computing environment. The parallelized version of the genetic algorithm is efficient and can be used to substantially reduce the computing time requirement for the calibration procedure. Therefore, it greatly enhances the potential applicability for large scale problems. An empirical study on the performance of the algorithm was conducted, from which an empirical formulae was developed to indicate the likely computing time in relation to the number of processors used for parallel computation.
genetic algorithm calibration lowry model parallel computing land-use and transportation