漂浮式风机锚泊系统的分析与优化设计涉及诸多变量，通常依赖经验以迭代试错法进行，在设计阶段需要耗费大量的计算资源和时间成本。该文提出了综合考虑锚泊系统安全性能、定位性能以及经济性能的多目标锚泊系统优化方法，并基于遗传算法和锚泊分析程序开发了锚泊系统智能优化设计程序；以运行在中国南海海域50 m水深的15 MW大功率漂浮式风机为研究对象，利用智能优化程序开展锚泊系统优化设计研究，并建立漂浮式风机时域全耦合数值模型，考虑工作工况、极端工况和破断工况等典型工况，比较分析锚泊系统优化方案和原方案的经济成本以及对漂浮式风机动力响应的影响。结果表明，锚泊系统优化方案的经济成本较该风机原有锚泊方案降低了17%，且定位性能和安全性能都有显著提升，进而证明了该文提出的锚泊系统智能优化方法和程序具有可行性和有效性，可为漂浮式风机锚泊系统优化设计提供技术支撑和指导作用。

Objective: Offshore wind energy is considered an attractive solution for power generation and environmental conservation. Floating offshore wind turbines (FOWTs) are feasible systems when the water depth exceeds 60 m, where the total cost for the bottom-fixed wind turbine increases. FOWTs usually keep position by using mooring systems, which provide up to 80% of the total damping in surge under certain circumstances. Furthermore, the mooring system accounts for approximately 20%—30% of the overall cost of an FOWT. Since the mooring system performance significantly affects the operational safety, power generation efficiency, and economic cost of an FOWT, it is necessary to select an appropriate mooring system based on engineering standards and requirements. The analysis and optimal design for the mooring system of a floating wind turbine involve many factors and variables and are usually conducted by trial-and-error method, requiring amounts of computational resources and time. Methods: This study proposes a multiobjective optimization method for the mooring system of a floating wind turbine, considering station-keeping ability, safety performance, and economic performance. Then, the intelligent optimization design program for this problem is developed based on the genetic algorithm and quasistatic mooring analysis code. On the basis of the aforementioned methods, the mooring system of a 15-MW floating wind turbine that was designed for a water depth of 50 m in the South China Sea is redesigned and optimized. The motion responses, safety performance, and cost of the original and optimized designs are compared and analyzed under operating, extreme, and breaking conditions. Results: The results show the following: First, the intelligent optimization program of the mooring system effectively achieves the optimization objectives and meets the requirements, as well as solves the complex multiobjective and multivariable problems in the mooring design process, which can significantly save the time and computing resources. Second, considering the construction and installation costs of the mooring line and anchor, the economic cost of the optimized solution is 17% lower than that of the original one, demonstrating significant economic improvement. Moreover, the damping of surge and sway of the optimized design is 69.30% and 21.43% greater than that of the original design, respectively, which is beneficial for reducing the horizontal motion of the floating wind turbine and mooring tension amplitude. Finally, with the optimized mooring system, the pitch maximum of the floating wind turbine at rated wind speed is reduced by 10.28%, which could be beneficial for improving the power generation efficiency. Both the optimized and original mooring systems meet the design requirements, and the horizontal motion amplitude is in good agreement under extreme conditions. In addition, under breaking conditions, the amplitude of the surge and the pitch motion of the floating wind turbine with the optimized mooring system are obviously reduced, and the safety factor of the mooring line is increased by 9.61%, which significantly reduces the risk of dragging anchor and improves the performance. Conclusions: Based on the aforementioned research, the feasibility and superiority of the intelligent optimization mooring design method and program are verified. This study could provide an engineering approach for mooring optimal design of the FOWT.