The document presents an overview of Particle Swarm Optimization (PSO), a stochastic optimization technique inspired by the social behavior of swarms, such as birds. It details the algorithm's process involving particles that move through space, adjusting their velocities based on personal and peer experiences to find optimal solutions. Real-life applications and considerations regarding velocity constraints in PSO are also discussed.

1. Presentation by: V. K. Sinha and R. J. Singh
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2. 
Introduction
Details
Algorithm
Real Life applications
Conclusion
Bibliography
Contents
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3. 
Introduction
Details
Algorithm
Real Life applications
Conclusion
Bibliography
Contents
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4. 
 Particle Swarm Optimization (P.S.O.) also known as
Swarm Intelligence is an algorithm developed by
James Kennedy and Russell Eberhart in 1995.
 It is a robust stochastic optimization technique based
on the movement and intelligence of swarms. PSO
applies the concept of social interaction for problem
solving.
Introduction
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5. 
 There are a number of particles in this algorithm
which move around in space to search for the best or
optimum value. These particles are provided with
initial velocities and certain constants and values at
the beginning.
 Each particle of the system has a certain velocity and
learning constants. It then moves in the space,
randomly and then adjusts according to the
experience collected from other particles.
Introduction contd..
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6. 
Introduction
Details
Algorithm
Real Life applications
Conclusion
Bibliography
Contents
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7. 
 The main motivation of this form of algorithm came
from real life examples.
 Swarming is a natural phenomenon.
Details
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8. 
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9. 
 In real life we take the example of birds as observed
by Craig Reynold and proposed in 1995.
 He observed three main properties that the birds
behaved.
 Separation – Each bird is an individual particle and
do not collide with other birds.
 Alignment – They move in the same general
direction.
 Cohesion – They do not move away from the flock
and try to stick together.
Details contd…
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10. 
 In this method each particle in space keeps track of
their position, and also of their neighbors. This
knowledge is used further to know a better position
(optimization of solution), this method combines
self-experiences and social experiences.
Details contd…
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11. 
 Each particle adjusts its travelling speed dynamically
corresponding to the flying experiences of itself and
its colleagues.
 Each particle changes 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.
Details contd…
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12. 
Introduction
Details
Algorithm
Real Life applications
Conclusion
Bibliography
Contents
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13. 
1. First we create a population of agents or particles to
make a swarm, uniformly distributed over a space
Pi.
2. Evaluate the position of each particle according to
objective function.
3. If a particle’s current position is better than its initial
position, then update it.
4. Determine best particle position (according to
particle’s previous best position)
Algorithm
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14. 
5. Update particles position
Algorithm contd…
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p: particle position
v: velocity
w: weight coefficient (0.1 to 0.9 )
c1: cognitive learning constant
c2: social learning constant
pbest: best position of the particle
gbest: best position of the swarm
Rand(U1 and U2): a random variable

15. 
w= wmax-(wmax-wmin)*iterationcurrent/iterationmax
Pbest(t+1) = pbest(t) if f(Pi(t)) > f(pbest(t))
Pi(t+1) if f(Pi(t)) =< f(pbset(t))
6. Move particles to new positions.
7. until stopping criteria are met.
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Algorithm contd…

16. 
 Nature is the best teacher.
 Ant colonies thrive due to this phenomenon.
 Birds also use swarm intelligence to survive.
 Fishes also exhibit this kind of behavior.
 PSO is used in computer science to optimize certain
functions.
Real Life applications
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17. 
 In all we can use particle swarm optimization for
finding optimum solutions to problems.
 Constraints to be kept in mind are that velocity
should have and optimum value, as too less will be
too slow, and if the velocity is too high then the
method will become unstable.
Conclusion
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18. 
 A presentation on Swarm Intelligence – from Natural
to Artificial Systems. - Ukradnuté kde sa dalo, a
adaptované
 A presentation on PSO by Maurice Clerk.
 The Particle Swarm Optimization Algorithm by
Andry Pinto, Hugo Alves, Inês Domingues, Luís
Rocha, Susana Cruz
Bibliography
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