Particle swarm optimization is an evolutionary computational technique based on the behavior of animal flocks. It was developed by Eberhart and Kennedy in 1995 and has been widely researched and utilized ever since. The algorithm is a stochastic optimization technique in which the most basic concept is that of particle. A particle represents an individual (i.e., fish or bird) that has the ability to move through the defined problem space and represents a candidate solution to the optimization problem. At a given point in time, the movement of particles is defined by their velocity, which is represented as a vector and therefore has magnitude and direction. This velocity is determined by the best position in which the particle has been so far and the best position in which any of the particles has been so far. Based on this, it is imperative to be able to measure how good (or bad) a particle’s position is; this is achieved by using a fitness function that measures the quality of the particle’s position and varies from problem to problem, depending on the context and requirements.