In the multi-objective optimization discipline (MOD), simultaneous optimization with four or more objectives is referred to as many-objective optimization. Compared with the two-objective and three-objective optimization, many-objective optimization brings a series of new challenges, such as the deterioration of the global search ability of an optimization algorithm, the difficulty of the visualization of Pareto solutions, and the increase of the computational burden. To address these challenges, in this study, an efficient many-objective optimization system was proposed, and this system was utilized to improve the aerodynamics of a Vista-class cruise ship at four crucial wind angles. In the process of design optimization, a parametric model with eight design variables was selected as the initial ship form. The uniform design (UD) sampling technique was employed to design a group of transformed ship forms. The Reynolds-averaged Navier-Stokes (RANS) solver was used to evaluate the aerodynamics of transformed ship forms, and the nearest neighbor mesh (NNM) interpolation method was utilized for the initialization process of each numerical calculation to reduce the computational cost of a single simulation. With the numerical results of all transformed ship forms, four aerodynamic surrogate models were established, using a combined method based on a particle swarm optimization and a radial basis neural network (PSO-RBFNN), to replace large-scale numerical simulation. In addition, the Sobol’ method was introduced to conduct the sensitivity analysis of the design variables. A series of mature genetic algorithms (GAs) were applied for the single-point, two-point, and four-point optimization of the aerodynamics of a cruise ship in sequence. Additionally, the optimal ship form was selected from the Pareto solutions of the four-point optimization based on the quantitative results of the analytical hierarchy process (AHP). Eventually, the dedicated experimental results of the optimized ship form showed that the aerodynamics at the four wind angles were improved together, confirming the effectiveness of the many-objective optimization system.

在多目标优化学科 (MOD) 中，具有四个或更多目标的同时优化被称为多目标优化。与二目标和三目标优化相比，多目标优化带来了一系列新的挑战，如优化算法的全局搜索能力变差、帕累托解的可视化困难、计算负担。为了应对这些挑战，本研究提出了一种高效的多目标优化系统，该系统用于改善 Vista 级游轮在四个关键风角下的空气动力学性能。在设计优化过程中，选择了具有八个设计变量的参数化模型作为初始船型。采用统一设计（UD）抽样技术设计了一组变形船型。采用雷诺平均纳维-斯托克斯 (RANS) 求解器对变换后的船型进行空气动力学评估，每个数值计算的初始化过程采用最近邻网格 (NNM) 插值法，以降低单次计算的计算成本。模拟。利用所有变换船型的数值结果，采用基于粒子群优化和径向基神经网络（PSO-RBFNN）的组合方法建立了四种气动代理模型，以替代大规模数值模拟。此外，还引入了 Sobol 方法来进行设计变量的敏感性分析。一系列成熟的遗传算法（GAs）被应用于单点，依次对游轮的空气动力学进行两点和四点优化。此外，根据层次分析法（AHP）的定量结果，从四点优化的帕累托解中选出最优船型。最终，优化后的船型的专用实验结果表明，四个风角下的空气动力学性能得到了共同改善，证实了多目标优化系统的有效性。