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

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

Swarm Intelligence

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)

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

Particle Swarm Optimization(PSO)

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. 1942-1945.

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

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.

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.

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

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.

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

PSO Algorithm

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

PSO Algorithm

simulation 1 x y fitness min max search space

simulation 2 x y fitness min max search space

simulation 3 x y fitness min max search space

simulation 4 x y fitness min max search space

simulation 5 x y fitness min max search space

simulation 6 x y fitness min max search space

simulation 7 x y fitness min max search space

simulation 8 x y fitness min max search space

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.

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

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

Application: Image Enhancement Face Recognition Travelling salesman problem

Application 1:

Application 2:

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.

Application 3:

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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