A B S T R A C TIn recent years, machine learning (ML) algorithms have been used tominimize maintenance costs and identify problems early in the auto-motive sector. The breakdown of a component or equipment impactsthe performance and cost, and hence it is considered a crucial stepin various domains. To fulfill this purpose, different approaches canbe considered to improve the application in real-world problems. Thedetermination of an asset’s residual useful life of a component at aspecific time is known as "remaining useful life" (RUL). The exten-sive evolution of data makes it challenging to analyze and interprethigh-level and valuable features from the data. The issue arises inall disciplines, and the automotive industry is no exception, giventhe large number of sensors to consider. Existing RUL research hasnot given much thought to the influence of high dimensionality dataon component maintenance and deterioration. The fundamental pur-pose of feature selection (FS) is to select a subset of features from thedata without compromising model performance. Lately, the trend ofresearch in feature selection has been based on bio-inspired methods.This work proposes a hybrid approach to the FS problem that com-bines Ant Colony Optimization (ACO) and Particle Swarm Optimiza-tion (PSO). When tested on public datasets, our results demonstrate arise in regression accuracy and a reduction in the number of selectedfeatures. ACO-PSO applies advantage from ACO to handle directlywith nominal attributes and uses advantage from PSO to handle con-tinuous features, utilizing the "the best of both worlds" in a singlealgorithm. This approach is combined with the random forest classi-fier for choosing the relevant and appropriate features. To gain betterresults from the algorithm, the performance of these approaches iscompared and the feature set with the best accuracy has been usedfor regression tasks. The results are evaluated in 5 public domaindatasets as well, and results show an increase in accuracy for the re-gression and the reduction of selected features number.