A review on particle swarm optimization algorithm and its devolopments

The optimization is mathematical technique that minimizing or maximizing some parameters of importance from the feasible region. In other words optimization is the selection of a best element on the bunch of alternatives. Particle Swarm Optimization (PSO) is a relatively new, efficient, robust and simple optimization algorithm which proves to work efficiently well on many of these optimization problems. Particle Swarm Optimization is a stochastic multi point search algorithm which models the social behavior of the birds flocking or fish schooling for food. It is widely used to find the global optimum solution in a complex search space. A large number of studies have been done to improve its performance This paper contains the theoretical idea and explanation of the different types of PSO algorithms, selection of the various parameters and their influences, controlling the convergence behaviors of PSO. This paper discussed the advantages and disadvantages of each method tried to highlight them. This paper reviews some kinds of improved versions as well as recent progress in the development of the PSO