Feature selection can be seen as a multi-objective task, where the goal is to select a subset of features that exhibit minimal correlation among themselves while maximizing their correlation with the target label. Multi-objective particle swarm optimization algorithm (MOPSO) has been extensively utilized for feature selection and has achieved good performance. However, most MOPSO-based feature selection methods are random and lack knowledge guidance in the initialization process, ignoring certain valuable prior information in the feature data, which may lead to the generated initial population being far from the true Pareto front (PF) and influence the population’s rate of convergence. Additionally, MOPSO has a propensity to become stuck in local optima during the later iterations. In this paper, a novel feature selection method (fMOPSO-FS) is proposed. Firstly, with the aim of improving the initial solution quality and fostering the interpretability of the selected features, a novel initialization strategy that incorporates prior information during the initialization process of the particle swarm is proposed. Furthermore, an adaptive hybrid mutation strategy is proposed to avoid the particle swarm from getting stuck in local optima and to further leverage prior information. The experimental results demonstrate the superior performance of the proposed algorithm compared to the comparison algorithms. It yields a superior feature subset on nine UCI benchmark datasets and six gene expression profile datasets.

特征选择可以看作是一个多目标任务，其目标是选择一个特征子集，这些特征之间的相关性最小，同时与目标标签的相关性最大化。多目标粒子群优化算法（MOPSO）已广泛应用于特征选择并取得了良好的性能。然而，大多数基于MOPSO的特征选择方法都是随机的，并且在初始化过程中缺乏知识指导，忽略了特征数据中某些有价值的先验信息，这可能导致生成的初始种群远离真实的帕累托前沿（PF）并影响人口的收敛速度。此外，MOPSO 在以后的迭代过程中有陷入局部最优的倾向。本文提出了一种新颖的特征选择方法（fMOPSO-FS）。首先，为了提高初始解的质量并增强所选特征的可解释性，提出了一种在粒子群初始化过程中结合先验信息的新颖初始化策略。此外，提出了一种自适应混合突变策略，以避免粒子群陷入局部最优并进一步利用先验信息。实验结果表明，与对比算法相比，所提出的算法具有优越的性能。它在 9 个 UCI 基准数据集和 6 个基因表达谱数据集上产生了卓越的特征子集。