Abstract
In our research of this article, the efficiency of Multi-objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Genetic Algorithm (NSGA-II), and Multi-objective Shuffled Complex Evolution Metropolis (MOSCEM-UA) algorithms were compared by implementing the Hydrological Model (HYMOD) and the related observed daily precipitation, evaporation and runoff data. High flow Nash-Sutcliffe efficiency and Low flow Nash-Sutcliffe efficiency were used to optimize the model parameters as two criterions; the time consumption, the dominating rate and, the quality of Pareto set (distance, distribution, and extent) were used to analyze the performance of the three algorithms. Compared with NSGA-II and MOSCEM-UA, the MOPSO algorithm performed most efficiently to complete each trail. The non-dominant solutions derived from MOPSO algorithm were seldom dominated by those from the other two algorithms, while a high proportion of solutions drawn from NSGA-II are dominated by the other two algorithms. When we come to the three optimization goal of multi-objective optimization, there is a complication. The shortest distance of the resulting non-dominated set to the Pareto-optimal front was from the NSGA-II algorithm the most uniform distribution of the solutions was derived from the MOSCEM-UA algorithm; and the maximal extent of the obtained non-dominated front was stemmed from the MOPSO algorithm. The results demonstrated that all three algorithms were able to find a good approximation of the Pareto set of solutions, but differed in the rate of convergence to the optimal solutions.
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References
Wagener, T., Wheater, H.S., Gupta, H.V.: Rainfall-Runoff Modeling in Gauged and Ungauged Catchments. Imperial College Press, London (2004)
Gupta, H.V., Sorroshian, S., Yapo, P.O.: Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. Journal of Hydrologic Engineering 4(2), 135–143 (1999)
Muletha, M.K., Nicklow, J.W.: Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model. Journal of Hydrology 306, 127–145 (2005)
Boyle, D.P., Gupta, H.V., Sorooshian, S.: Toward improved calibration of hydrolog-ical models: Combining the strengths of manual and automatic methods. Water Resources Research 36(12), 3663–3674 (2000)
Srinivas, N., Deb, K.: Multi-objective function optimization using non-dominated sorting genetic algorithms. Evolutionary Computation 2, 221–248 (1995)
Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Coello Coello, C.A., Lechuga, M.S.: MOPSO: A proposal for multiple objective particle swarm optimization. In: Proceedings of international Conference on Evolutionary Computation, pp. 1051–1056 (2002)
Vrugt, J.A., Gupta, H.V., Bastidas, L.A., et al.: Effective and effi cient algorithm for multiobjective optimization of hydrologic models. Water Resources Research 39, 1–19 (2003)
Tang, Y., Reed, P., Wagener, T.: How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration. Hydrology and Earth System Sciences Discussions 2, 2465–2520 (2005)
Moore, R.J.: The probability-distributed principle and runoff production at point and basin scale. Hydrological Sciences Journal 30(2), 273–297 (1985)
Bos, A., Vreng, A.: Parameter optimization of the HYMOD model using SCEM-UA and MOSCEM-UA. University of Amsterdam, Amsterdam (2006)
Smakhtin, V.Y., Sami, K., Hughes, D.A.: Evaluating the performance of a deterministic daily rainfall-runoff model in a low flow context. Hydrology Process 12(5), 797–811 (1998)
Zitzler, E., Thiele, L.: An evolutionary algorithm for multiobjective optimization: The strength pareto approach. Techinical Report 43. Computer Engineering and Communication Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland. TIK-Report, No.43 (1998)
Zitzler, E., Deb, K., Thielel, L.: Comparison of multi-objective evolutionary algorithms: empirical results. IEEE Transaction on Evolutionary Computation 18(2), 173–195 (2008)
Raquel, C.R., Naval Jr., P.C.: An effective use of crowding distance in multiobjective particle swarm optimization. In: Proceedings of the Genetic and Evolutionary Computation (GECCO 2005), Washington, DC, USA (2005)
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Huang, X., Lei, X., Jiang, Y. (2012). Comparison of Three Multi-objective Optimization Algorithms for Hydrological Model. In: Li, Z., Li, X., Liu, Y., Cai, Z. (eds) Computational Intelligence and Intelligent Systems. ISICA 2012. Communications in Computer and Information Science, vol 316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34289-9_24
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DOI: https://doi.org/10.1007/978-3-642-34289-9_24
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