Particle Swarm Optimization (PSO) is population-based metaheuristic which is highly popular and most efficient among all the available metaheuristics nowadays. It has been applied successfully to solve complex optimization problems in many fields of science and technology. Since its inception in 1995, the performance of PSO has been improved with several extensions and enhancements leading to its various variations. In this chapter, the basic framework of PSO along with the related literature review and bibliometric analysis is introduced first. Later, a few variations of PSO and numerical examples are presented to depict the usefulness of PSO in solving some unconstrained and constrained optimization problems. Finally, discussions of PSO’s practical uses are included.