飞机在飞越含有过冷水滴的云层时，机翼、发动机进气道表面容易发生结冰，危害飞行安全。热气防冰是应用最为广泛的结冰防护手段之一，为了保证在较低的引气量下仍能满足防冰要求，提出了基于引气控制的热气防冰优化设计方法。基于本征正交分解和径向基函数神经网络建立热气防冰性能快速预测方法，实现对防冰优化设计空间内结构参数变化后对应表面温度、溢流水质量流量分布等的快速评估。基于差分进化算法、快速非支配排序遗传算法开展热气防冰系统的单目标优化设计和多目标优化设计，并使用热气防冰性能快速预测方法评估种群个体的目标函数和约束函数。在满足优化问题约束条件的前提下，单目标优化设计所得热气防冰结构较基础设计所需引气量下降了23.41%；多目标优化设计可以得到一系列优化的防冰结构，设计人员可以通过评估减小引气量和增强防冰性能两方面目标的权重来选择最为合适的热气防冰结构设计方案。

In-flight icing occurs on wings and engine inlets when aircraft fly through clouds containing supercooled water droplets， which is detrimental to flight safety. Hot air anti-icing is one of the most widely used anti-icing technologies. To ensure that anti-icing requirements are met with lower bleed air quantities， an optimization design method based on bleed air control for hot air anti-icing system is developed. A fast prediction method for hot air anti-icing performance based on Proper Orthogonal Decomposition and Radial Basis Function neural networks is constructed， enabling rapid evaluation of the surface temperature and runback water mass flow rate distributions for different anti-icing configurations within the anti-icing optimization design space. Genetic algorithms and Nondominated Sorting Genetic Algorithms are employed for the single- and multi-objective optimization problems of the hot air anti-icing system. The fast prediction method for hot air anti-icing performance is used to evaluate the objective and constraint functions of individuals in the population. While satisfying the constraint functions of the optimization problem， the single-objective optimized design can reduce the required bleed air quantity by 23.41% compared to the baseline design. The multi-objective optimization design produces a series of optimized anti-icing configurations， and the design engineers can choose the most suitable anti-icing configuration design by evaluating the weights of both bleed air reduction and anti-icing performance enhancement.