IJAPRR International Peer Reviewed Refereed Journal, Vol. II, Issue IV, p.n. 1-6, 2015 Page 1 International Journal of Allied Practice, Research and Review Website: www.ijaprr.com (ISSN 2350-1294) Study on Particle Swarm Optimization Om Prakash 1 and Sajal Kumar Das 2 Abstract - Particle swarm optimization is a stochastic, population-based computer problem-solving algorithm; it is a kind of swarm intelligence that is based on social- principles and provides insights into social behavior, as well as contributing to social-psychological engineering applications. The aim of this paper is to give fundamental insight into the particle swarm optimization algorithm Keywords: Particle swarm optimization, Evolutionary computing, inertia weight I. Introduction The last three decades have witnessed the development in efficient and effective stochastic optimizations. In contrast to the traditional adaptive stochastic search algorithms, evolutionary computation (EC) techniques exploit a set of potential solutions, namely a population, and detect the optimal solution through cooperation and competition among the individuals of the population. These techniques often detect optima in difficult optimization problems faster than traditional methods [3]. One of the most powerful swarm intelligence-based optimization techniques, named PSO, was introduced by Kennedy and Eberhart [1, 2]. PSO is inspired by the swarming behavior of animals, and human social behavior. During the last decade many studies focused on this method and almost all of them, strongly confirmed the abilities of this newly proposed optimization technique [1, 3, 4, 7], e.g. fast convergence, finding global optimum in presence of several local optima, simple programming and adaptability with constrained problems. Some author attempted to enhance the algorithm by developing new variations such as variable inertia coefficient, constriction factor [4], maximum velocity limit, parallel 1 optimization, deflection, repulsion, stretching [2], mutation [7,8] etc. Particle swarm optimization was invented by Russ Eberhart and James Kennedy in 1995 through simplifying a social simulation model which was originally developed to simulate the process of birds seeking food. The PSO algorithm is a population-based evolutionary algorithm. Like other evolutionary algorithms, each individual (called particle in PSO) in the population represents a candidate solution to the problem to be solved. Unlike other evolutionary * Asstt. Prof., ECE Department, JJT University, Jhunjhunu (Raj.) ** Modam R&D, Ericsson (I) PVT Ltd., Outer Ring Road,Bangalore