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 c ity of Hong Kong was employed for demonstrating the performance of the para llelized genetic algorithm, in terms of two commonly used performance measu res: speedup and efficiency. The genetic algorithm is particularly suitable for implementation under a parallel computing environment. The parallelize d version of the genetic algorithm is efficient and can be used to substant ially 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 conduc ted, from which an empirical formulae was developed to indicate the likely computing time in relation to the number of processors used for parallel co mputation. (C) 2001 Published by Elsevier Science B.V.