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1 Shuffled Complex Evolution method (SCE-UA) A global optimization algorithm J. Nossent.
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Transcript of 1 Shuffled Complex Evolution method (SCE-UA) A global optimization algorithm J. Nossent.
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Shuffled Complex Evolution method (SCE-UA)
A global optimization algorithm
J. Nossent
2Pag.
Global optimization
Optimize OF over ENTIRE parameter space– RANDOM sampling (deal with local optimums)
– SLOWER than local methods (2000 – 10 000 runs)
SCE-UA– Widespread in hydrology
– Implemented in SWAT2005
– Information sharing by SHUFFLING key to efficient algorithm
3Pag.
SCE-UA
Developed at the University of Arizona (UA)
Combines strength of:– Nelder-Mead (simplex)
– Controlled random search
– Genetic algorithms
– Complex shuffling
4Pag.
SCE-UA
Complex: – subgroup of sample set
Ω:– Parameter space
5Pag.
SCE-UA
6Pag.
SCE-UA
7Pag.
SCE-UA
8Pag.
SCE-UA
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SCEM-UA
SCE-UA + Metropolis algorithm– No longer to small area
– Allows simulation of posterior density
Metropolis– MCMC
– Replaces Simplex method
10Pag.
References
SCE-UA– Duan, Q., Gupta, V.K. and Sorooshian, S., 1993. A shuffled complex evolution
approach for effective and efficient global optimization, J.,Optim. Theory Appl., 76, p501-521
– Sorooshian, S. and Gupta, K.V., 1995. Model Calibration. In: Computer models of watershed hydrology, chapter 2 . Singh, V.P. (editor).Water resources publications
SCEM-UA– Vrugt, J. A., Gupta, H.V., Bouten, W. and Sorooshian, S., 2003. A Shuffled Complex
Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39 (8), 1201, doi:10.1029/2002WR001642
– Vrugt, J. A., H. V. Gupta, L. A. Bastidas, W. Bouten and S. Sorooshian, 2003. Effective and efficient algorithm for multi objective optimization of hydrologic models, Water Resour. Res., 39 (8), 1214, doi:10.1029/2002WR001746