Comment on "How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?" by Y. Tang et al., Hydrol. Earth Syst. Sci., 10, 289–307, 2006

Abstract

jectives to create the initial sample of points. The results presented in Vrugt et al. (2003) have demonstrated that this alternative search strategy provides a computational efficient and robust alternative to multiobjective optimization. It is surprising however that TRW have not considered this second or alternative optimization strategy in their paper. I believe that this alternative sampling approach should have been used in conjunction with the various algorithms, to appropriately disseminate and implement the ideas presented in Vrugt et al. (2003). If correctly used, this alternative search strategy would have provided the entire Pareto tradeoff surface as depicted in Fig. 5, at far less computational costs than the SPEA2 and "-NSGAII algorithms. For example, preliminary analyses of the identification problem discussed in Fig. 5, suggest that state-of-the-art single objective search algorithms can identify the single criterion solutions of RMSE(R) and RMSE(T) in less than 20 000 function evaluations. Experience further suggests that about 2000 additional function evaluations would have been needed to sample the entire Pareto front using this prior information. This is considerably less than the 15 000 000 number of SAC-SMA model evaluations used to construct the results presented in TRW. Thus, in practice, this alternative search strategy using prior information from the single criterion ends of the Pareto front would have consistently received superior performance to the SPEA2 and "-NSGAII algorithms. This would especially be true for the hydrologic model calibration problems, discussed in case study (2) and (3). Nevertheless, the work presented in TRW addresses a number of critical issues related to the use of multiobjective optimization for the calibration of hydrologic models, and highlights the strengths and weaknesses of current available evolutionary search algorithms. In response to this, we (Vrugt and Robinson, 2007) have recently developed a new method called A Multi-ALgorithm Genetically Adaptive Multiobjective or AMALGAM method, that combines two new concepts, simultaneous multi-method search,