Date of Graduation

5-2017

Level of Access

Open Access Thesis

Department or Program

Computer Science

First Advisor

Stephen Majercik

Abstract

Particle Swarm Optimization (PSO) is often used for optimization problems due to its speed and relative simplicity. Unfortunately, like many optimization algorithms, PSO may potentially converge too early on local optima. Using multiple neighborhoods alleviates this problem to a certain extent, although premature convergence is still a concern. Using dynamic topologies, as opposed to static neighborhoods, can encourage exploration of the search space at the cost of exploitation. We propose a new version of PSO, Dynamic-Static PSO (DS-PSO) that assigns multiple neighborhoods to each particle. By using both dynamic and static topologies, DS-PSO encourages exploration, while also exploiting existing knowledge about the search space. While DS-PSO does not outperform other PSO variants on all benchmark functions we tested, its performance on several functions is substantially better than other variants.