Kontrol Cerdas Dr.Mat Syaiin ST.MT

1. Introduction to Particles
Swarm Optimization
Presented By Mat S

2. Particle Swarm Optimization
Inventors: James Kennedy and Russell Eberhart
 An Algorithm originally developed to imitate the
motion of a Flock of Birds, or insects
 Assumes Information Exchange (Social Interactions)
among the search agents
 Basic Idea: Keep track of
– Global Best (G best)
– Self Best (P best)

3. How does it work?
 Problem:
Find X which minimizes f(X)
 Particle Swarm:
– Start: Random set of solution vectors
– Experiment: Include randomness in the choice
of new states.
– Remember: Encode the information about good
solutions.
– Improvise: Use the ‘experience’ information to
initiate search in a new regions

4. Particle Swarm Optimization

5. Particle Swarm Optimization
Vi pbest and Vik

6. Particle Swarm Optimization

7. Overview of PSO
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8. Example
Find minimum value in the function:
(x - 15)^2 + (y - 20)^2 = 0
Answer:
x = 15 and y = 20
Now.. Please find minimum value using PSO!

9. Example
1st iterations
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10. Example
Iteration X Y (X-15)^2+(Y-20)^2 Gbest
1 10 10 125 25
2 10 14.9721 50.27981356 25
3 10 23.77597 39.25795364 15.7092

11. Example
0 0 0.7403 1 0 2 0.7404 10 10 2 0.7404 10 10
0 0 0.2934 1 0 2 0.2934 10 10 2 0.2934 10 10
For X

12. Example
4.972 6.464 0.7404 1 6.4637 2 0.7404 10 10 2 0.7404 10 10
8.804 11.445 0.2934 1 11.445 2 0.2934 14.97 14.97 2 0.2934 10 10
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13. Example
2nd iterations
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14. Example
7th Iterations
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15. Example
30th Iterations
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16. Particle Swarm Optimization
What is P best ?
What is G best ?
Look at the datapbestngbest.xls

17. Demo
Thank You