Use Particle Swarm Optimization to Optimize Data Search

Abstract

In computer science and engineering Particle Swarm Optimization (PSO) is a very good clustering for swarm optimization. This is very easy to implement &there are few parameters to adjust. The particle swarm optimization concept consists of, at each time step, changing the velocity of (accelerating) each particle toward its pBest and lBest locations (local version of PSO). Acceleration is weighted by a random term, with separate random numbers being generated for acceleration toward pBest and lBestlocations. In past several years, PSO has been successfully applied in many research and application areas. It is demonstrated that PSO gets better results in a faster, cheaper way compared with other methods. Another reason that PSO is attractive is that there are few parameters to adjust. One version, with slight variations, works well in a wide variety of applications. Particle swarm optimization has been used for approaches that can be used across a wide range of applications, as well as for specific applications focused on a specific requirement. We actually used some of the features of basic PSO. In basic PSO velocity measure is a very important fact as well as position update. But we have made a modified version of PSO by using some of the features of the general PSO because general PSO is not so much comfortable with our dataset. III