process. Thus, approaches that include a multi-fidelity surrogate model have been widely studied [6; 7; 8] in aerospace engineering. Multi-fidelity methods for single-objective optimization based on the error estimation of response surfaces have been successfully applied to design low-boom supersonic jets [6]. Because the response surfaces [9] use simple concepts to construct the function, the multi-fidelity function based on the response surface is easy to construct. However, such methods are often associated with low accuracy because the response surface model cannot be optimized in situations involving an extremely small number of data points. A co-kriging model [7; 8] has been extensively applied to combine multi-fidelity functions. In addition, it has been employed to solve an optimal single-objective airfoil design problem [7]. This method is beneficial for predicting a complicated landscape function. However, it is less optimal in predicting a smooth landscape function. [PERSON] proposed a multi-fidelity single-objective optimization process for wind turbine design [10; 11]. This optimization process begins with the location of an optimal point of a low-fidelity function by using a genetic algorithm (GA). Then, an optimization with a high-fidelity function is performed using a gradient-based method. The optimum point of the low-fidelity function is used as a starting point. This method is beneficial, as it uses a basic optimization tool to solve the multi-fidelity optimization problem. However, it could fail to find an optimum high-fidelity function if the error between the low-fidelity and high-fidelity functions is large. Because the gradient-based method required many evolution functions to solve multi-objective optimization problems, the multi-fidelity optimization based on evolutionary computation combined with the gradient method would not reduce the computation time of the high-fidelity function. Thus, multi-fidelity approaches are expected to improve the efficiency. An approach to multi-fidelity/multi-objective optimization involves a model reduction technique [12; 13]. This technique has been applied to helicopter rotor blade design and airfoil design [13; 14]. The application of this technique reduces the design parameter space for defining the possible design ranges based on a low-fidelity function. A high-fidelity function is then used to find the optimum design in the primary defined design range. The high-fidelity function is sampled by selecting the preferred design points, where the blade shows improved performance with respect to the baseline design, in order to ensure good diversity of the sample data, in addition to the initial point that is generated with the primary defined design range. However, this approach has the potential to obtain an unexpected optimal solution outside the parameter space given that the design ranges of the low-fidelity function are not always appropriate for the high-fidelity function. [PERSON] [15] used GA to find the optimum solution of low-fidelity data and selected the interesting points of the optimum solution using the low-fidelity data to find the optimum point of high-fidelity data of a hovering rotor airfoil design. Efficient global optimization (EGO) [16] is a widely-used method that consists of kriging-model-based explorations. EGO involves additional sampling-procedure-based expected improvements (EIs) for improving the model accuracy. The EIs are defined for single-objective optimization. The expected hypervolume improvement (EHVI) algorithm [17; 18] has been proposed to improve the non-dominated front defining the expectations of hypervolume improvements (HVI); it considers the model uncertainty, as well as the EIs. However, existing studies have not investigated the applicability of EHVI to multi-fidelity techniques. In this article, a multi-objective optimization process involving multi-fidelity/multi-objective EGO is proposed and investigated through the solutions of test functions and airfoil design problems.