# Particle Swarm Optimization

### From Glossary

(PSO). This is an evolutionary algorithm
based on swarm behavior of animals, like bird flocking. A move from a state is influenced by directions of states in its neighborhood. A *consensus function* is used to average neighbors' best fitness values, and this is combined with the original state's fitness to obtain a new position for the state. It applies to continuous variables, using a *velocity* term to derive state increments. The fundamental equation that governs state evolution is

where p is a particle, or state, and

velocity vector of particle |

position vector of particle |

previous position of particle giving best fitness value |

position of globally best fitness value |

= parameter, called "inertia weight" |

are pseudo-random numbers in |

positive parameters, generally in |