2. (Particle Swarm Optimisation)
PSO
(Particle Swarm Optimisation)
استاد راهنما :
سرکار خانم مهندس سبزواری
تهيه و تنظيم:
فاطمه علي اكبري- الهام خاکشور

3. Particle Swarm Optimization(PSO)
Swarm Intelligence
History
Origins and Inspiration of PSO
What is PSO?
Algorithm
PSO and GA Comparison
PSO – Pros and Cons
Applications

4. Swarm Intelligence

5. Swarm Intelligence
Swarm Intelligence (SI) is the property of a system whereby
the collective behaviors of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge.
Two main Swarm Intelligence based methods
Particle Swarm Optimization (PSO)
Ant Colony Optimization (ACO)

6. Swarm Intelligence Characteristics of a swarm:
Distributed, no central control;
Limited communication
No (explicit) model of the environment;
Ability to react to environment changes.
Social interactions (locally shared knowledge) provides the basis for problem solving

7. Particle Swarm Optimization(PSO)

8. History
Russ Eberhart
James Kennedy
Kennedy, J. and Eberhart, R., “Particle Swarm Optimization,” Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia 1995, pp

9. Origins and Inspiration of PSO
Population based stochastic optimization technique inspired by social behaviour of bird flocking or fish schooling.
Related to bird flocking, fish schooling and swarming theory
- steer toward the center
- match neighbors’ velocity
- avoid collisions

10. Suppose a group of birds are randomly searching food in an area.
There is only one piece of food in the area being searched.
All the birds do not know where the food is. But they know how far the food is in each iteration.
So what's the best strategy to find the food? The effective one is to follow the bird which is nearest to the food.

11. What is PSO?
In PSO, each single solution is a "bird" in the search space.
Call it "particle".
All of particles have fitness values
which are evaluated by the fitness function to be optimized, and
have velocities
which direct the flying of the particles.
The particles fly through the problem space by following the current optimum particles.

12. Population-based search procedure in which individuals called particles change their position (state) with time.
 individual has position
& individual changes velocity

13. Particles fly around in a multidimensional search space.
During flight, each particle adjusts its position according to its own experience, and according to the experience of a neighboring particle, making use of the best position encountered by itself and its neighbor.

14. Particle Swarm Optimization (PSO) Process
Initialize population in hyperspace
Evaluate fitness of individual particles
Modify velocities based on previous best and global (or neighborhood) best positions
Terminate on some condition
Go to step 2

15. PSO Algorithm

16. Social (global) influence
inertia
Personal influence
PSO Algorithm
Update each particle, each generation
v[i] =w* v[i] + c1 * rand() * (pbest[i] - x[])
+ c2 * rand() * (gbest[i] - x[i])
x[i] = x[i] + v[i]
where c1 and c2 are learning factors (weights) a
Social (global) influence b

17. PSO Algorithm

18. simulation 1 x y
fitness
min
max
search space

19. simulation 2 x y
fitness
min
max
search space

20. simulation 3 x y
fitness
min
max
search space

21. simulation 4 x y
fitness
min
max
search space

22. simulation 5 x y
fitness
min
max
search space

23. simulation 6 x y
fitness
min
max
search space

24. simulation 7 x y
fitness
min
max
search space

25. simulation 8 x y
fitness
min
max
search space

26. PSO and GA Comparison 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.

27. PSO and GA Comparison Differences
PSO does not have genetic operators like crossover and mutation. Particles update themselves with the internal velocity.
They also have memory, which is important to the algorithm.
Particles do not die
the information sharing mechanism in PSO is significantly different
Info from best to others, GA population moves together

28. PSO – Pros and Cons Simple in concept Easy to implement
Computationally efficient
Application to combinatorial problems

29. Application:
Image Enhancement
Face Recognition
Travelling salesman problem

30. Application 1:

32. Application 2:

33. Image Enhancement Using Particle Swarm Optimization
Abstract—Applications of the Particle Swarm Optimization (PSO) to solve image processing problem with a reference to a new automatic enhancement technique based on real-coded particle swarms is proposed in this paper. The enhancement process is a non-linear optimization problem with several constraints. The objective of the proposed PSO is to maximize an objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The feasibility of the proposed method is demonstrated and compared with Genetic Algorithms (GAs) based image enhancement technique. The obtained results indicate that the proposed PSO yields better results in terms of both the maximization of the number of pixels in the edges and the adopted objective evaluation. Computational time is also relatively small in the PSO case compared to the GA case.

34. Application 3:

35. Particle swarm optimization-based algorithms
for TSP and generalized TSP
Abstract A novel particle swarm optimization (PSO)-based algorithm for the traveling salesman problem (TSP) is presented. An uncertain searching strategy and a crossover eliminated technique are used to accelerate the convergence speed. Compared with the existing algorithms for solving TSP using swarm intelligence, it has been shown that the size of the solved problems could be increased by using the proposed algorithm. Another PSO-based algorithm is proposed and applied to solve the generalized traveling salesman problem by employing the generalized chromosome. Two local search techniques are used to speed up the convergence. Numerical results show the effectiveness of the proposed algorithms.

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