Automatic calibration of a rainfall-runoff model using a fast and elitist multi-objective particle swarm algorithm

Authors:
Yang Liu
Affiliations:
Manchester Interdisciplinary Biocentre, Department of Engineering and Physical Sciences, University of Manchester, UK
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

In order to successfully calibrate a numerical model, multiple criteria should be considered. Multi-objective genetic algorithms (MOGAs) have proved effective in numerous such applications, where most of the techniques relying on the condition of Pareto efficiency to compare different solutions. In this paper, a new non-dominated sorting particle swarm optimisation (NSPSO), is proposed, that combines the operations (fast ranking of non-dominated solutions, crowding distance ranking and elitist strategy of combining parent population and offspring population together) of a known MOGA NSGA-II and the other advanced operations (selection and mutation operations) with a single particle swarm optimisation (PSO). The efficacy of this algorithm is demonstrated on the calibration of a rainfall-runoff model, and the comparison is made with the NSGA-II. The simulation results suggest that the proposed optimisation framework is able to achieve good solutions as well diversity compared to the NSGA-II optimisation framework.