1. Shuffled Complex Evolution method (SCE-UA) A global optimization algorithm
J. Nossent

2. Global optimization Optimize OF over ENTIRE parameter space SCE-UA
RANDOM sampling (deal with local optimums)
SLOWER than local methods (2000 – runs)
SCE-UA
Widespread in hydrology
Implemented in SWAT2005
Information sharing by SHUFFLING key to efficient algorithm

3. SCE-UA Developed at the University of Arizona (UA)
Combines strength of:
Nelder-Mead (simplex)
Controlled random search
Genetic algorithms
Complex shuffling

4. SCE-UA
Complex:
subgroup of sample set Ω:
Parameter space

5. SCE-UA

6. SCE-UA

7. SCE-UA

8. SCE-UA

9. SCEM-UA SCE-UA + Metropolis algorithm Metropolis
No longer to small area
Allows simulation of posterior density
Metropolis
MCMC
Replaces Simplex method

10. References SCE-UA SCEM-UA
Duan, Q., Gupta, V.K. and Sorooshian, S., A shuffled complex evolution approach for effective and efficient global optimization, J.,Optim. Theory Appl., 76, p
Sorooshian, S. and Gupta, K.V., 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., A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39 (8), 1201, doi: /2002WR001642
Vrugt, J. A., H. V. Gupta, L. A. Bastidas, W. Bouten and S. Sorooshian, Effective and efficient algorithm for multi objective optimization of hydrologic models, Water Resour. Res., 39 (8), 1214, doi: /2002WR001746