concepts and working of particle swarm optimization.

1. Particle Swarm
Optimization
(PSO)

2. Introduction
 Many difficulties such as multi-
modality, dimensionality and
differentiability are associated with the
optimization of large-scale problems.
 Traditional techniques such as
steepest decent, linear programing
and dynamic programing generally fail
to solve such large-scale problems
especially with nonlinear objective
functions.

3. Introduction…
 Traditional techniques often fail to
solve optimization problems that have
many local optima.
 To overcome these problems, there is
a need to develop more powerful
optimization techniques.

4. Introduction…
 Some of the well-known population-
based optimization techniques are:
 Genetic Algorithms (GA)
 Artificial Immune Algorithms (AIA)
 Ant Colony Optimization (ACO)
 Particle Swarm Optimization (PSO)
 Bacteria Foraging Optimization (BFO)
 Artificial Bee Colony (ABC)
 Biogeography-Based Optimization (BBO)
Etc.

5. Particle Swarm Optimization
(PSO)
 Particle swarm optimization (PSO) is
an evolutionary computation technique
developed by Kennedy and Eberhart.
 It exhibits common evolutionary
computation attributes including
initialization with a population of
random solutions and searching for
optima by updating generations.

6. Concept
 A Simulation of a simplified social
system.
 The original intent was to graphically
simulate the graceful but
unpredictable choreography of a bird
flock.
 Each particle keeps track of its
coordinates in the problem space,
which are associated with the best
solution (fitness) it has achieved so

7. How it works ??
 PSO is initialized with a group of
random particles (solutions) and then
 Searches for optima by updating
generations.
 Potential solutions, called particles,
are then ‘‘flown’’ through the problem
space by following the current
optimum particles.

8. How it Works ??
 Each particle keeps track of its
coordinates in the problem space, which
are associated with the best solution
(fitness) it has achieved so far.
 This value is called ‘pBest’.
 Another "best" value that is tracked by
the particle swarm optimizer is the best
value obtained so far by any particle in
the population.
 This second best value is a global best
and called “gbest”.

9. How it works ??
 The particle swarm optimization
concept consists of, at each step,
changing the velocity (i.e.
accelerating) of each particle toward
its ‘pBest’ and ‘gBest’ locations (global
version of PSO).

10. PSO Algorithm (General)
Searches Hyperspace of Problem for
Optimum
 Define problem to search
 How many dimensions?
 Solution criteria?
 Initialize Population
 Random initial positions
 Random initial velocities
 Determine Best Position
 Global Best Position
 Personal Best Position
 Update Velocity and Position Equations

11. The step-by-step
implementation

12. Step 1:
 Initialize PSO parameters which are
necessary for the algorithm.
 population size which indicates the
number of individuals,
 number of generations necessary for the
termination criterion,
 cognitive constant, social constant,
 variation of inertia weight, maximum
velocity,
 number of design variables and
respective ranges for the design
variables.

13. Step 2:
 Generate random population equal to
the population size specified.
 Each population member contains the
value of all the design variables. This
value of design variable is randomly
generated in between the design
variable range specified.
 population means the group of birds
(particles) which represents the set of
solutions.

14. Step 3:
 Obtain the values of the objective function for
all the population members.
 For the first iteration, value of objective
function indicates the pBest for the respective
particle in the solution.
 Identify the particle with best objective
function value which identifies as gBest.
 If the problem is a constrained optimization
problem, then a specific approach such as
static penalty, dynamic penalty and adaptive
penalty is used to convert the constrained
optimization problem into the unconstrained
optimization problem.

15. Step 4:
 Update the velocity of each particle
and Check for the maximum velocity.
 If the velocity obtained exceeds the
maximum velocity,
 then reduce the existing velocity to the
maximum velocity.

16. Step 5:
 Update the position of the particles,
 Check all the design variables for the
upper and lower limits.

17. Step 6:
 Obtain the value of objective function
for all the particles.
 The new solution replaces the pBest if
it has better function value.
 Identify the gBest from the population.
 Update the value of inertia weight if
required.

18. Step 7:
 Best obtained results are saved using
elitism.
 All elite members are not modified
using crossover and mutation
operators but can be replaced if better
solutions are obtained in any iteration.

19. Step 8:
 Repeat the steps (from step 4) until
the specified number of generations or
termination criterion is reached.

20. Advantages
 PSO is based on the intelligence. It can
be applied into both scientific research
and engineering use.
 PSO have no overlapping and mutation
calculation.
 The search can be carried out by the
speed of the particle. During the
development of several generations, only
the most optimist particle can transmit
information onto the other particles, and
the speed of the researching is very fast.

21. Advantages…
 The calculation in PSO is very simple.
Compared with the other developing
calculations, it occupies the bigger
optimization ability and it can be
completed easily.
 PSO adopts the real number code,
and it is decided directly by the
solution. The number of the dimension
is equal to the constant of the solution.

22. Disadvantages
 The method easily suffers from the
partial optimism, which causes the less
exact at the regulation of its speed and
the direction.
 The method can not work out the
problems of scattering and
 The method can not work out the
problems of non-coordinate system,
such as the solution to the energy field
and the moving rules of the particles in
the energy field

23. Thank You !!