Particle swarm optimization (PSO) is a heuristic global optimization method, which is based on swarm intelligence. It is inspired by the research on the bird and fish flock movement behavior. The algorithm is widely used and can rapidly be implemented with a few parameters to be tuned. In PSO, individuals, referred to as particles, are “flown” through a hyper-dimensional search space. Changes to the position of particles within the search space are based on the social-psychological tendency of individuals to emulate the success of other individuals. The changes to a particle within the swarm are therefore influenced by the experience, or knowledge, of its neighbors. Many different PSO variants have been proposed in the past. This paper describes a few of these variants that have been implemented, and compares them with standard PSO on a number of benchmark functions measuring both the solution quality and execution time.