28-06-2012, 03:14 PM
Particle Swarm Optimization
PSO.ppt (Size: 99 KB / Downloads: 92)
Particle Swarm Optimization (PSO) applies to concept of social interaction to problem solving.
It was developed in 1995 by James Kennedy and Russ Eberhart
It has been applied successfully to a wide variety of search and optimization problems.
In PSO, a swarm of n individuals communicate either directly or indirectly with one another search directions (gradients).
PSO is a simple but powerful search technique.
Particle Swarm Optimization:Swarm Search
In PSO, particles never die!
Particles can be seen as simple agents that fly through the search space and record (and possibly communicate) the best solution that they have discovered.
So the question now is, “How does a particle move from on location in the search space to another?”
This is done by simply adding the v-vector to the x-vector to get another x-vector (Xi = Xi + Vi).
Once the particle computes the new Xi it then evaluates its new location. If x-fitness is better than p-fitness, then Pi = Xi and p-fitness = x-fitness.
Particle Swarm:Controlling Velocities
When using PSO, it is possible for the magnitude of the velocities to become very large.
Performance can suffer if Vmax is inappropriately set.
Two methods were developed for controlling the growth of velocities:
A dynamically adjusted inertia factor, and
A constriction coefficient.
Particle Swarm Optimization:Swarm and Neighborhood Size
Concerning the swarm size for PSO, as with other ECs there is a trade-off between solution quality and cost (in terms of function evaluations).
Global neighborhoods seem to be better in terms of computational costs. The performance is similar to the ring topology (or neighborhoods greater than 3).
There has been little research on the effects of swarm topology on the search behavior of PSO.