Automatic calibration a hydrological model using a master–slave swarms shuffling evolution algorithm based on self-adaptive particle swarm optimization

Parameter estimation for hydrological models is a challenging task, which has received significant attention by the scientific community. This paper presents a master–slave swarms shuffling evolution algorithm based on self-adaptive particle swarm optimization (MSSE-SPSO), which combines a particle swarm optimization with self-adaptive, hierarchical and multi-swarms shuffling evolution strategies. By comparison with particle swarm optimization (PSO) and a master–slave swarms shuffling evolution algorithm based on particle swarm optimization (MSSE-PSO), MSSE-SPSO is also applied to identify HIMS hydrological model to demonstrate the feasibility of calibrating hydrological model. The results show that MSSE-SPSO remarkably improves the calculation accuracy and is an effective approach to calibrate hydrological model.

► A master–slave swarms shuffling evolution algorithm based on self-adaptive particle swarm optimization (MSSE-SPSO) is proposed.
► MSSE-SPSO combines particle swarm optimization with self-adaptive, hierarchical and complex shuffling evolution strategies.
► MSSE-SPSO is applied to calibrate HIMS hydrological model for verifying its feasibility.
► The results show that MSSE-SPSO remarkably improves the calculation accuracy compared with PSO and MSSE-PSO.
► The proposed MSSE-SPSO is an effective approach to calibrate hydrological model.