Particle swarm optimization is a computational algorithm used to optimize a solution through sequential processing of particles using a specified estimation of quality. The algorithm is inspired by biological systems such as bird and insect swarms. This paper focuses on the comparison between Local PSO and Global PSO. We have utilized eight functions to provide benchmarks to compare the optimizations provided by the two optimization strategies. This resulted in findings that indicate that global optimizations tend to be more effective than local optimizations when comparing final costs. Our research indicates that an automated approach to particle swarm optimization will benefit from employing a range of benchmark functions and implementing both local and global optimizations. Analysis of the various particle topologies are discussed, and benchmark functions are selected and analyzed in regard to their final costs, as well as the overall particle topologies that they produce.