Additionally, incorporating FS in TC systems will help reduce classification complexity and processing demands [25; 26]. Over the past years, researchers have been challenged with finding robust FS methods, relevant features and classifiers to enhance the performance of the TC system. This problem occurs due to many available FS methods and techniques in the literature. Obviously, executing this trail is a complicated and time-consuming task. Therefore, this necessitates the development of a technique to find an optimal solution, among others. Recently, optimization techniques such as Particle Swarm Optimization (PSO) [27] have been used to solve selected problems across several domains [28]. These techniques are known to emulate natural evolution in their operations. PSO is a comparative evolutionary algorithm (EA) based on swarm intelligence, which is considered one of the most efficient search methods over various proposed method in the literature. Moreover, it is not very expensive and can converge faster in comparison with other EA [29]. This is the main motivation behind using the PSO method. Additionally, it has been successfully applied in feature selection [30], ensemble learning [31], and clustering [32]. Therefore, this work provides a new technique for ATC that uses a meta-heuristic algorithm to find the best answer from a variety of feature selection methods and classifiers using a set of features. This configuration is determined using the PSO [33]. The proposed method, called OCATC, consists of three phases covering data preparation, experiment, and testing and evaluation. In the first phase, OCATC starts by preparing a given Arabic dataset using several preprocessing tasks, including tokenization, normalization, stop word removal, and stemming, then extracts features from the dataset using the TF-IDF approach. In the second phase, the dataset is divided into train and test sets using 10-fold cross-validation, whereby the training set is used during the learning of PSO to find the optimal configuration. This step is considered the main contribution, where the PSO begins by generating a set of solutions, and each solution represents a configuration of three elements: the feature selection method, number of features, and classifier. Then, the fitness function for each solution is computed to determine the best configuration. After that, it updates the position and velocity of each solution until the prerequisites for stopping are met. Finally, the testing set is used to evaluate the quality of the best configuration to build the classification system using the optimal FS method (first element), the optimal number of features (second element), and the optimal classifier (third element). The recommended approach, to the best of our knowledge, aims to find an effective solution for the ATC system to automatically select the optimal solution from a set of elements such as feature selection methods, features, and classifiers, which has not been applied before. The purpose of this study is to suggest an alternate way, namely OCATC, based on a PSO algorithm to come up with the best solution for the ATC job. OCATC finds the optimal solution among three elements sets, including feature selection methods, number of features, and classifiers. Then, we evaluated the proposed method using publicly available datasets. Finally, a series of experiments were performed to show the effectiveness of the proposed method. The key contributions of this research might be summarized as follows:  
* Classification is presented as a single objective optimization problem where the search region is divided into three elements set covering feature selection methods, number of features, and classifiers. The optimal combination of elements from these separate classes can be retrieved by PSO algorithm;
* An alternative method (OCATC) is proposed using improved Particle Swarm Optimization (PSO) algorithm to find the optimal combination (solution) for ATC;
* The optimal solution for a set of publicly available datasets is presented.