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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*…show more content…*

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 directs 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 updates generations to search for optimal solution . Each particle is updated by two "best" values in each iteration. The first value is one is the best solution (fitness) it has achieved so far it is called pbest and this value is stored . The second value is another "best" value that is traced by the particle swarm optimizer is the best value obtained in the population so far by any particle. This best value called the gbest is the global best. When a particle takes part of the population as its topological*…show more content…*

The objective of algorithm was to search for an optimal path in a graph in the way ants seek the path between their home and source of food. When the ants search for food they initially explore the area surrounding their nest in a random fashion. When the ants find the source of food they evaluate the quantity and quality of food. During the return journey the ants deposit a chemical called pheromone trial on the ground in order to mark some favorable path that should be followed by other members of the colony. When the other members travelling at random find such a path they leave the random behavior and follow the path. After some time the pheromone trail begins to evaporate due to which its attractive strength decreases. If the ant takes more time to travel to path and then back again then pheromone strength decreases as during more time the pheromone strength decreases. As compared to short path it gets marched over more frequently and thus the density of pheromone becomes higher on shorter paths than longer paths. Pheromone evaporation also prevents the convergence to a locally optimal solution .without the mechanism of pheromone evaporation the paths chosen by the first ants would tend to be excessively attractive to the following ones. In that case

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 directs 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 updates generations to search for optimal solution . Each particle is updated by two "best" values in each iteration. The first value is one is the best solution (fitness) it has achieved so far it is called pbest and this value is stored . The second value is another "best" value that is traced by the particle swarm optimizer is the best value obtained in the population so far by any particle. This best value called the gbest is the global best. When a particle takes part of the population as its topological

The objective of algorithm was to search for an optimal path in a graph in the way ants seek the path between their home and source of food. When the ants search for food they initially explore the area surrounding their nest in a random fashion. When the ants find the source of food they evaluate the quantity and quality of food. During the return journey the ants deposit a chemical called pheromone trial on the ground in order to mark some favorable path that should be followed by other members of the colony. When the other members travelling at random find such a path they leave the random behavior and follow the path. After some time the pheromone trail begins to evaporate due to which its attractive strength decreases. If the ant takes more time to travel to path and then back again then pheromone strength decreases as during more time the pheromone strength decreases. As compared to short path it gets marched over more frequently and thus the density of pheromone becomes higher on shorter paths than longer paths. Pheromone evaporation also prevents the convergence to a locally optimal solution .without the mechanism of pheromone evaporation the paths chosen by the first ants would tend to be excessively attractive to the following ones. In that case

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