Particle Swarm optimizat ion. Cooperation example. Outline. Basic Idea of PSO Neighborhood topologies Velocity and position Update rules Choice of parameter value Applications. The basic idea. Each particle is searching for the optimum
Particle Swarm optimization
Particles Adjust their positions according to a ``Psychosocial compromise’’ between what an individual is comfortable with, and what society reckons
My best perf.
Here I am!
The best perf. of my neighbours
The right way
Or this way
How to choose parameters
(5—30) are reported as usually sufficient.
Some functions often used for testing real-valued optimisation algorithms
... and some typical results
Best result after 40 000 evaluations
Adaptive swarm size
I try to kill myself
There has been enough improvement
although I'm the worst
I try to generate a new particle
I'm the best
but there has been not enough improvement
The better I am, the more I follow my own way
The better is my best neighbour, the more I tend to go towards him