The recognition of supersonic inlet flow pattern has become a research hotspot in recent years. In this paper, the dual external pressure supersonic inlet is taken as the research object. To explore the flow characteristics of the inlet, time-mean processing on the inlet pressure signal collected by sensors is conducted first, and the features of the inlet pressure data in time domain and frequency domain are extracted, respectively. As feature selection (FS) plays an important role in classification tasks and has been recently studied as a multi-objective optimization problem, two objectives of FS are considered and an improved non-dominated sorting genetic algorithm NSGA2 with hybrid mutation operators using support vector machines (SVM) as classifiers is proposed, aiming to simultaneously select feature subsets and optimize SVMs hyper-parameters. In addition, a way to deal with variation transgression is proposed to make the mutation operator of the single-objective evolution fit well in the multi-objective evolution algorithm. Experimental results on 31 sensor datasets demonstrate that our proposed algorithm can achieve competitive classification accuracy while obtaining a smaller size of feature subset compared with particle swarm optimization algorithm and some multi-objective optimization algorithms using single-objective evolution mutation operators.

中文翻译：

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基于支持向量机的混合变异非支配排序遗传算法的超音速进气流识别

超音速进气流型识别已成为近年来的研究热点。本文以双外压超音速进气道为研究对象。为探究进口的流动特性，首先对传感器采集的进口压力信号进行时间均值处理，分别提取进口压力数据的时域和频域特征。由于特征选择（FS）在分类任务中起着重要作用，并且最近作为多目标优化问题被研究，因此考虑了 FS 的两个目标，以及使用支持向量机的具有混合变异算子的改进的非支配排序遗传算法 NSGA2 （SVM）作为分类器被提出，旨在同时选择特征子集并优化 SVM 超参数。此外，提出了一种处理变异越界的方法，使单目标进化的变异算子在多目标进化算法中得到很好的拟合。在 31 个传感器数据集上的实验结果表明，与粒子群优化算法和一些使用单目标进化变异算子的多目标优化算法相比，我们提出的算法可以在获得更小的特征子集大小的同时实现有竞争力的分类精度。