Implementation and Performance Analysis of Particle Swarm Optimization Algorithm Using MATLAB

PSO utilises the population of partiocles that fly through hyperspace with given velocities.. As described by Eberhart and Dr. James Kennedy, the PSO algorithm is an adaptive algorithm based on social psychological metaphor; a population of individual (referred to as particles) adapts by returning stochastically towards previously successful regions. PSO is a computational intelligence based technique that is not largely affected by the size and non linearity of the problem and can converge to the optimal solution in many problems where most analytical methods fail to converge. PSO learned from the scenario of bird flocking and used it to solve the optimization problems. In PSO, each single solution is a "bird" in the search space. We 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. In this paper we are describing a basic PSO algorithm and the algorithm is simulated with mat lab .The simulations are performed with different parameters. Results and Graphs clearly show that the PSO Algorithms are effective for optimization problems. Algorithm has Simple implementation, easily parallelized for concurrent processing, Derivative free with few algorithm parameters, Very efficient global search algorithm, higher accuracy (mesh densities) and higher throughput.