Comparison of Three Multiobjective Optimization Algorithms for Inverse Modeling of Vadose Zone Hydraulic Properties

摘要: Inverse modeling has become increasingly popular for estimating effective hydraulic properties across a range of spatial scales. In recent years, many different algorithms have been developed to solve complex multiobjective optimization problems. this study, we compared the effi ciency Nondominated Sorting Genetic Algorithm (NSGA-II), Multiobjective Shuffl ed Complex Evolution Metropolis algorithm (MOSCEM-UA), and AMALGAM, multialgorithm genetically adaptive search method estimation soil parameters. our analyses, implemented HYDRUS-1D model used observed pressure head data at three depths from Spydia experimental fi eld site in New Zealand. Our problem was posed context by simultaneously using complementary RMSE criteria each depth. We analyzed trade-off between these adherent Pareto uncertainty. The results demonstrate that all were able fia good approximation set solutions, but differed rate convergence distribution. Small differences performance various because relative high dimension combination with presence multiple local optimal solutions within three-objective space. parameter sets yielded satisfactory when simulating transient tensiometric predetermined observation points investigated vadose zone profi le. overall best found AMALGAM values 0.14, 0.11, 0.17 m 0.4-, 1.0-, 2.6-m depths, respectively. contrast, t errors substantially higher respective ranging 0.87 1.49 m, parameters derived laboratory analysis small cores.