Particle Swarm Optimification Case Study

PARTICLE SWARM OPTIMIZATION

Particle swarm optimization ( PSO) is a heuristic global optimization technique put forward by Doctor Kennedy and E berhart in 1995. it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. It is developed from swarm intelligence and was inspired by the movement of behavior of bird and fish flock. PSO simulates the bird flocking behavior. Consider the following situation a group of birds are randomly searching food in an area suppose in area being searched there is only one piece of food and no bird knows where the food is. But they know how far the food is in each iteration performed. So the best strategy to find the food is to keep an eye on the bird which is nearest to the food. PSO acquired the knowledge from the scenario and used it in finding the solutions for optimization problems. In Particle Swarm Optimization each single solution is a "bird" in the search space called "particle". Each particle has a fitness value associated with it which is evaluated by the fitness function to be optimized which direct the flying of the particles and have velocities. The particles follow the current optimum particles and fly through the problem space.The initial value assigned to the PSO is a group of arbitrary/random particles (solutions) then it update generations to search for optimal solution . Each particle is updated by two "best" values in