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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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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

  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