Automatic calibration of a rainfall–runoff model using a fast and elitist multi-objective particle swarm algorithm
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...
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| Veröffentlicht in: | Expert systems with applications Jg. 36; H. 5; S. 9533 - 9538 |
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01.07.2009
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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. |
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| AbstractList | 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. |
| Author | Liu, Yang |
| Author_xml | – sequence: 1 givenname: Yang surname: Liu fullname: Liu, Yang email: sharkyangliu916@hotmail.com organization: Manchester Interdisciplinary Biocentre, Department of Engineering and Physical Sciences, University of Manchester, UK |
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| Cites_doi | 10.1109/IJCNN.2007.4371107 10.1145/1068009.1068047 10.2166/wst.1997.0163 10.1007/978-3-540-28651-6_80 10.1016/S0022-1694(97)00107-8 10.1016/S0022-1694(00)00279-1 10.1007/3-540-45105-6_4 10.1016/0304-3800(95)00084-9 10.1109/ICNN.1995.488968 10.1109/4235.996017 10.1109/CEC.2000.870279 10.1109/TEVC.2004.826067 |
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| Keywords | Multiple objectives Parameter estimation Rainfall–runoff models Calibration Optimisation |
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| Title | Automatic calibration of a rainfall–runoff model using a fast and elitist multi-objective particle swarm algorithm |
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