This document provides an overview of particle swarm optimization (PSO) techniques. It discusses how PSO was inspired by bird flocking and fish schooling behavior. The basic PSO algorithm is described as maintaining a swarm of particles where each particle represents a potential solution. Particles adjust their position based on their own experience and the experiences of neighboring particles. Variations of PSO algorithms including local best and global best approaches are also summarized. Advantages and disadvantages of PSO are listed. An example case study applying PSO to an optimization problem is also included.