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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| Published in: | Expert systems with applications Vol. 36; no. 5; pp. 9533 - 9538 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.07.2009
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| Subjects: | |
| ISSN: | 0957-4174, 1873-6793 |
| Online Access: | Get full text |
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| Summary: | 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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| Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| ISSN: | 0957-4174 1873-6793 |
| DOI: | 10.1016/j.eswa.2008.10.086 |