The complexity in real-world problems motivated researchers to innovate efficient problem-solving techniques. Generally  natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization and Non-deterministic polynomial hard (NP-Hard) problems because of their ability to adjust to variety of conditions. This paper shows an overview for swarm based algorithm that based on ant behavior. The first algorithm that inspired ant behavior in search for food source was developed in 1992 and was tested in solving TSP problem  Ant Colony Optimization  (ACO) is a metaheuristic inspired by some ant species' pheromone trail laying and following behavior. Artificial ants in ACO are stochastic solution construction processes that use (artificial) pheromone information that is modified depending on the ants' search experience and possibly accessible heuristic information to generate candidate solutions for the problem instance under consideration. Many notable research achievements have been gained since the proposal of the Ant System, the first ACO algorithm. These contributions concentrated on the creation of high-performance algorithmic variants, the creation of a generic algorithmic framework for ACO algorithms, the successful application of ACO algorithms to a wide range of computationally difficult problems, and the theoretical understanding of ACO algorithm properties. Since the appearance of ACO many modifications for improving the performance of the algorithm and has been applied to various Applications in several fields. At the end of this paper, the improvements are listed  

The spread of the COVID-19 pandemic broughtsignificant changes in society. Emerging technologies like artificial intelligence and machine learning devices improved several industries, especially in academe and higher education institutions. In this study, a model to analyze and predict college students' sentiments from the Flexible Learning Experience portal was built using several supervised machine-learning techniques. Waikato Environment for Knowledge Analysis (WEKA) application was used to apply the Naive Bayes (NB), C4.5, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms. Additionally, a comparative analysis of different machine-learning methods was applied. The experimental results revealed that the C4.5 algorithmobtained the highest accuracy than other algorithms. The effectiveness of each algorithm was evaluated and compared using 10-fold crossvalidation (CV), taking into account the major accuracy metrics, instances that were accurately or inaccurately classified, kappa statistics, mean absolute error, and modeling time. Moreover, results show that the C4.5 algorithm outperformed other algorithms by classifying the model with 98.13% accuracy, 0.0132 mean absolute error, and 0.00 seconds of training time. Furthermore, teachers and college administrations were well accustomed to the sentiments and problems of college students and might act as a decisionsupport mechanism mainly as they deal with the new setting during this time of crisis.

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