Particle Swarm Optimization - PSO

The Particle Swarm Optimization by Kennedy and Eberhardt is inspired by swarm intelligent behaviour seen in animals like birds or ants. A swarm of particles is a set of individual agents "flying" across the search space with individual velocity vectors. There is no selection as in classic Evolutionary Algorithms. Instead, the individuals exchange knowledge about the space they have come across. Each one is attracted to the best position the individual has seen so far (cognitive component) and to the best position known by its neighbors (social component).
The neighborhood is defined by the swarm velocity, which may be a linear ordering, a grid and some others. The influence of the velocity of the last time-step is taken into account using an inertness/ constriction parameter, which controls the convergence behaviour of the swarm. The influence of social and cognitive attraction are weighed using the phi parameters. In the constriction variant there is a dependence enforced between constriction and the phi, making sure that the swarm converges slowly but steadily, see the publications of Clerc, e.g.