<p>Swarm optimization plays a significant role in finding the optimal solution for real-life problems in evolutionary computing. It is one of the innovative and intelligent techniques inspired by the biological behavior of swarms. These algorithms are developed with the social behavior of swarms, for example, birds and fish while searching for food and their communication. Swarm optimization algorithms efficiently solve real-life problems; they gradually converge to a local minimum in a high-dimensional search space. Therefore, it has become a challenging task for basic swarm optimization algorithms like particle swarm optimization. Much research has been carried out in many directions with some modified features. This review paper discusses variations of swarm optimization algorithms, their hybridization with different concepts, and applications along with fundamental concepts, operations, and working principles. The prime objective of this research is to provide different directions and growth of swarm optimization algorithms.</p>