Selection of genetic algorithm operators for urban drainage model parameter optimisation

Abstract

Recently, genetic algorithms (GAs) have proven to be successful and efficient in identifying the optimal parameters for water resource modelling applications. However, in order to produce efficient and robust solutions, proper selection of GA operators for the application is necessary, before conducting the model parameter optimisation. General guidelines are available for standard GA optimisation applications. However, there is no specific guidance available for selecting GA operators for urban drainage model parameter optimisation. Therefore, the sensitivities of these operators were analysed through numerical experiments by repetitive simulation considering one GA operator at a time, by integrating GA and urban drainage modelling software. The tested GA operators in this study were the population size, the number of generations, the number of model parameter sets to be considered from the final generation to determine the optimum set, the selection type and the crossover and mutation rates. It was found that urban drainage models with a small number of parameters (i.e. two or less) could be optimised with any of the tested GA operator sets. However, the proper selection of GA operators is vital to the convergence of the optimum model parameters, for urban drainage models with a large number of parameters (i.e. five or more).