Year of Graduation
2017
Level of Access
Open Access Thesis
Embargo Period
5-20-2017
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.
Included in
Artificial Intelligence and Robotics Commons, Numerical Analysis and Scientific Computing Commons, Theory and Algorithms Commons