Besides, DL algorithms are being used in rotating machine failure diagnosis due to their ability to classify data without specific tools for extracting signal characteristics [5, 6, 23, 24]. In [PERSON] and [PERSON] [5], Convolutional neural networks (CNN) are used to diagnose bearing defects in TIMs. These networks learn the information in the vibration signals' spectra, thus compacting the data needed for the classification process. On the other hand, [PERSON] et al. [6] presented an alternative model called stacked autoencoders sparse filter rotating component comprehensive diagnosis (SAFC) to identify bearing defects in electric rotating machines. The proposed methodology has the ability to perform clustering and reduce the size of the spectra obtained through machine vibration signals. In [PERSON] et al. [24], a deep-belief convolutionary network (CBDN) approach to extracting and learning the relevant characteristics of bearing fault signals in electrical machines was presented. The main advantages of CBDN over other DL approaches are that they are quickly processed to perform calculations and extract features. Specifically, in this work, the researchers adopted Adam optimizer to accelerate the training model and the speed of convergence, making it possible to find an optimized model structure. In the experimental results, the classification accuracy values between 96% and 98% demonstrate the ability of this new approach in the diagnosis of bearing defects. Some works from different areas have recently employed swarm intelligence tools and meta-heuristics techniques to achieve ideal solutions for complex optimization problems. Among them, it can be mentioned particle swarm optimization (PSO), whale optimization (WO), wheel-based differential evolution (WDE), grasshopper optimization (GO), biogeography-based optimization (BBO), beetle antennae search (BAS), and artificial bee colony (ABC) algorithms [8, 14, 16, 17, 23, 25, 26, 27, 28]. In the work of [PERSON] et al. [23], the researchers used the PSO algorithm to optimize the multiple parameters of the model proposed to diagnose bearing defects in several rotating machines. The PSO algorithm'suse allowed an increase in the efficiency of the bearing failure diagnosis in TIMs subject to several operating conditions, such as variation in the operating speed ranges. It was obtained classification rates higher than 90%, which validates the proposed system. In [PERSON] et al. [25], a new approach called enhanced selective ensemble deep learning method with BAS algorithm was presented to diagnose train locomotive machine bearing faults. In this methodology, the BAS algorithm determines the optimal class-specific thresholds to perform the best classification. The experimental results showed that using this algorithm to the proposed method enabled a more accurate and robust recognition of the bearing fault patterns. The methodology correctly diagnosed more than 97% of all samples. It should be noted that there are no variations in the operating conditions of the machines. Recently, taking advantage of the WO algorithm's good optimization capacity, [PERSON] et al. [26] were able to define the appropriate limits for signal decomposition to extract the bearing failure information needed for proper diagnosis. In the work of [PERSON] and [PERSON] [14] previously described, the PSO and WDE algorithms allowed the identification of the most relevant features extracted through the feature selection tools used in the methodology. This approach presented classification rates of approximately 96% even in situations where the TIMs were subject to variations in the load, demonstrating the system's promising diagnostic capacity. Recently in the study of [PERSON] et al. [16], it was employed the GO optimization algorithm to improve the learning and generalization capacity of the SVM classifier. This optimization tool was used to determine two important parameters of the SVM that must be previously defined before its use in pattern classification.