1. Scientific Research Group in Egypt (SRGE)
Swarm Intelligence (I)
Particle swarm optimization
Scientific Research Group in Egypt (SRGE)
Dr. Ahmed Fouad Ali
Suez Canal University,
Dept. of Computer Science, Faculty of Computers and informatics
Member of the Scientific Research Group in Egypt

2. Scientific Research Group in Egypt

3. Meta-heuristics techniques

4. Outline 1. Swarm intelligence (Main idea)
2. History of Particle swarm optimization
3. Particle swarm optimization (PSO)
4. PSO Algorithm
5. Advantage / disadvantage
6. Comparison with Genetic algorithm
7. References

5. Swarm intelligence (Main Idea)
Suppose you and a group of friends are on a treasure finding mission. Each one in the group has a metal detector and can communicate the signal and current position to the n nearest neighbors.
Each person therefore knows whether one of his neighbors is nearer to the treasure than him. If this is the case, you can move closer to that neighbor. In doing so, your chances are improved to find the treasure. Also, the treasure may be found more quickly than if you were on your own.

6. Swarm intelligence (Main Idea)
A swarm can be defined as a structured collection of interacting organisms (or agents).
Within the computational study of swarm intelligence, individual organisms have included ants, bees, wasps, termites, fish (in schools) and birds (in flocks).

7. Swarm intelligence (Main Idea)
The global behavior of a swarm of social organisms therefore emerges in a nonlinear manner from the behavior of the individuals in that swarm
The interaction among individuals plays a vital role in shaping the swarm's behavior.
Interaction among individuals aids in refining experiential knowledge about the environment, and enhances the progress of the swarm toward optimality.

8. History of particle swarm optimization
Proposed by James Kennedy &
Russell Eberhart in 1995
Inspired by simulation social
behavior Related to bird flocking,
fish schooling and swarming theory
- steer toward the center
- match neighbors’ velocity
- avoid collisions
Combines self-experience with social experience
Population-based optimization

9. Particle swarm optimization (concepts)
Set of agents (particles) that
constitute a swarm moving around
in the search space looking for the
best solution
Each particle in search space adjusts
its “flying” according to its own
flying experience as well as the flying
experience of other particles

10. Particle swarm optimization (concepts)
Movement towards a promising area
to get the global optimum
Each particle keeps track:
Its best solution, personal best,
pbest
The best value of any particle,
global best, gbest

11. Particle swarm optimization (concepts)
Each particle modifies its position according to:
its current position
its current velocity
the distance between its current position and pbest
the distance between its current position and gbest

12. Particle swarm optimization (concepts)
Swarm: a set of particles (S)
Particle: a potential solution
Position, Velocity:
Each particle maintains
Individual best position (PBest)
Swarm maintains its global best (GBest)

13. Particle swarm optimization Algorithm
P = Particle_Initialization();
For i=1 to it_max
For each particle p in P do
fp = f(p);
If fp is better than f(pBest)
            pBest = p;
end
end gBest = best p in P;
v = v + c1*rand*(pBest – p) + c2*rand*(gBest – p);
p = p + v; end end

14. Particle swarm optimization Algorithm
Personal influences
Inertia
vi(t+1) = vi (t)+ c1*rand*(pBest(t) – p(t)) +
c2*rand*(gBest(t) – p(t));
Social influence
Particle’s velocity

15. PSO Algorithm (parameter setting)
The right way
This way
Or this way
Number of particles
(10—50) are reported as usually sufficient.
C1 (importance of personal best)
C2 (importance of neighborhood best)
Usually C1+C2 = 4.
Vmax – too low: too slow
too high: too unstable.

16. Advantage / disadvantage
Simple implementation
Slow convergence in refined search stage
Few parameters to adjust
Weak local search ability
Efficient in global search

17. Comparison with genetic algorithm (GA)
Commonalities
PSO and GA are both population based stochastic optimization
Both algorithms start with a group of a randomly generated population,
Both have fitness values to evaluate the population.
Both update the population and search for the optimium with random techniques.
Both systems do not guarantee success.

18. Comparison with genetic algorithm (GA)
Differences
PSO does not have genetic operators like crossover and mutation. Particles update themselves with the internal velocity.
Particles do not die.
The information sharing mechanism in PSO is significantly different
There is no selection in PSO

19. References Computational Intelligence An Introduction
Andries P. Engelbrecht, University of Pretoria South Africa
Some slides adapted from a presentation
“The Particle Swarm Optimization Algorithm” By Andry Pinto, Hugo Alves, Inês Domingues, Luís Rocha
Susana Cruz.
Particle Swarm Optimization

20. Thank you