Transcript Shuffled Complex Evolution method (SCE

Shuffled Complex Evolution
method (SCE-UA)
A global optimization algorithm
J. Nossent
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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
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SCE-UA
Developed at the University of Arizona (UA)
Combines strength of:
– Nelder-Mead (simplex)
– Controlled random search
– Genetic algorithms
– Complex shuffling
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SCE-UA
Complex:
– subgroup of sample set
Ω:
– Parameter space
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SCE-UA
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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
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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
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– 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
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