Computationally expensive multiobjective optimization problems are difficult to solve using solely evolutionary algorithms (EA) and require surrogate models, such as the Kriging model. To solve such problems efficiently, we propose infill criteria for appropriately selecting multiple additional sample points for updating the Kriging model. These criteria correspond to the expected improvement of the penalty-based boundary intersection (PBI) and the inverted PBI. These PBI-based measures are increasingly applied to EAs due to their ability to explore better nondominated solutions than those that are obtained by the Tchebycheff function. In order to add sample points uniformly in the multiobjective space, we assign territories and niche counts to uniformly distributed weight vectors for evaluating the proposed criteria. We investigate these criteria in various test problems and compare them with established infill criteria for multiobjective surrogate-based optimization. Both of the proposed criteria yield better diversity and convergence than those obtained with other criteria for most of the test problems. Index Terms-Efficient global optimization (EGO), expected improvement (EI), penalty-based boundary intersection (PBI), expensive optimization, multiobjective optimization.1089-778X (c)

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This study investigates the challenges and opportunities presented by downwind wind turbines and offers a roadmap of future research pathways to maximize their potential. Multidisciplinary design, analysis, and optimization comparison studies between upwind and downwind configurations on a modern 10‐MW offshore wind turbine are presented to support the discussion. On one hand, the downwind rotor is found to consistently have a smaller swept area under loading. As a result, the downwind design produces less annual energy production (−1.2%). On the other hand, lighter blades for the downwind configuration lead to lower capital costs (−1.7%), so there is little difference in the levelized cost of energy between the two. Key ultimate and fatigue loads are compared, with some values increasing in the downwind configuration, while others decrease. The impact of a downwind configuration on the tower and the impacts of cone and tilt angles and free‐yaw system on the levelized cost of energy are also investigated. The results show a mix of some advantages and disadvantages. Given these results, four areas of research in advanced controls, highly tilted rotors, higher fidelity aerodynamic models, and floating wind are proposed for downwind wind turbines.

A hybrid method between the Kriging model and the radial basis function (RBF) networks is proposed for robust construction of a response surface of an unknown function. In the hybrid method, RBF approximates the macro trend of the function and the Kriging model estimates the micro trend. Hybrid methods using two types of model selection criteria (MSC), i.e., leave-one-out cross-validation and generalized cross-validation for RBF were applied to three one-dimensional test problems. The results were compared with those of the ordinary Kriging (OK) model and the universal Kriging model. The accuracy of each response surface was compared by function shape and root mean square error. The proposed hybrid models were more accurate than the OK model for highly nonlinear functions because they can capture the macro trend of the function properly by RBF, while the OK model cannot. In addition, the hybrid models can find the global optimum with few sample points using the Kriging model approximation errors.