PARTICLE SWARM OPTIMIZATION
Particle Swarm Optimization (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
vp = wvp + A r (x*p – xp) + B r' (g* – xp ) where p is a particle, or state, and
| vp = velocity vector of particle p |
| xp = position vector of particle p |
| x*p = previous position of particle p giving best fitness value |
| g*p = position of globally best fitness value |
| w = parameter, called "inertia weight" |
| r, r' are pseudo-random numbers in [0,1] |
| A, B = positive parameters, generally in [1,2] |
