## 1 Introduction
[40] presented the hull form modification by means of bell-shaped distribution functions. [PERSON] and [PERSON] [41] proposed modifying the SAC using two trigonometric functions. [PERSON] and [PERSON] [38] devised curve-based hull form variation method in which the hull form curves can be modified with the sectional area and the centroid remains unchanged. [PERSON] et al. [17] and [PERSON] [42] proposed parametric modification function in the form of higher-order polynomial, which allows one to conduct the variations in SAC, U-V type section shape, design load waterline (DLWL) section shape, and bulb shape in a systematic manner. The second element of hull form SBD is the evaluation of hydrodynamic performance by numerical simulation. Depending on the cost function to be minimized, either potential flow solver or viscous flow RANS solver is employed. Potential flow solver for the evaluation of wavemaking resistance has been widely used for hull form optimization studies [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 15; 16; 17; 18; 19; 20; 21; 23; 25; 27]. From the wavemaking resistance coefficient \(C_{W}\) obtained from potential flow solver, the total resistance coefficient \(C_{TM}\) can be calculated by \(C_{TM}=(1+k)C_{FM}+C_{W}\). Here, the frictional resistance coefficient \(C_{FM}\) is calculated using the ITTC friction line. The form factor \((1+k)\) can be obtained in various ways: empirical formula [12; 13], model test results [11; 19], double body solution using RANS solver [27]. In spite of the advantage of shorter computational time, the accuracy issue due to negligence of viscosity makes potential flow solver less popular nowadays. On the other hand, the high-fidelity simulation of free surface RANS solver is widely adopted for the assessment of hydrodynamic performance. In the hull form SBD process with repeated evaluations for optimal hull form candidates, however, there are practical issues to be solved as follows; first, in addition to the high-performance cluster computing, additional considerations to further reduce computational time are required. For instance, the reduction of computational mesh during optimization process [8; 9] and the calculation with ship being fixed to running attitude [5] have been proposed. The second issue is the quality of computational mesh. In particular, the mesh quality should be maintained for each modified hull form during optimization. Regarding the last element of optimization, various methods can be selected depending on the cost function and the constraints. Sequential quadratic programming (SQP) is a gradient-based local optimization method which is frequently employed for single objective optimization [1; 4; 5; 6; 7; 13; 14; 15; 16; 17]. For multiple objective optimization, the Pareto optima are sought using either particle swarm optimization (PSO) or genetic algorithm (GA). The PSO is a derivative-free, probabilistic optimization method and has been adopted by [PERSON] et al. [17], [PERSON] and [PERSON] [18], [PERSON] et al. [20], and [PERSON] et al. [21]. The hull form SBD studies with GA are [PERSON] et al. [2], [PERSON] et al. [8], [PERSON] et al. [9], [PERSON] et al. [10], [PERSON] [12], [PERSON] et al. [19], [PERSON] et al. [23], [PERSON] et al. [24], [PERSON] and [PERSON] [25], and [PERSON] et al. [26]. A literature survey indicates that the existing hull form optimization studies have been predominantly based on potential flow solver [1; 2; 3; 4; 5; 6; 7; 10; 11; 12; 13; 15; 16; 17; 18; 19; 20; 21; 23; 24; 25; 27]. There are a few notable studies based on free surface RANS solver; [PERSON] et al. [8] minimized the total resistance of model \(R_{TM}\) of naval vessel (DTMB5414) based on GA method. [PERSON] et al. [9] conducted multi-objective optimization study for the same hull to minimize both \(R_{TM}\) and motion response amplitude operator (RAO). In order to save computational time, they used coarse grid for the iterative steps of optimization.