Particle swarm optimization

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

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Meta-heuristics techniques

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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

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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.

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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).

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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.

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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

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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

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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

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•
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

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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)

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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;
For each particle p in P do
v = v + c1*rand*(pBest – p) + c2*rand*(gBest – p);
p = p + v;
end
end

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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

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PSO Algorithm (parameter setting)
•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.

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

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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.

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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

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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
http://www.particleswarm.info/
http://www.swarmintelligence.org

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Thank you
Ahmed_fouad@ci.suez.edu.eg
http://www.egyptscience.net