Discrete optimization is a difficult task common to many different areas in modern research. This type of optimization refers to problems where solution elements can assume one of several discrete values. The most basic form of discrete optimization is binary optimization, where all solution elements can be either 0 or 1, while the more general form is problems that have solution elements which can assume $n$ different unordered values, where $n$ could be any integer greater than 1. While Genetic Algorithms (GA) are inherently able to handle these problems, there has been no adaption of Particle Swarm Optimization able to solve the general case.