Date of Graduation


Document Type


Degree Name

Bachelor of Science in Mechanical Engineering

Degree Level



Mechanical Engineering


Sha, Zhenghui

Committee Member/Reader

Jensen, David


The objective of this research is to develop a new approach in engineering complex swarm systems with desired characteristics based on the theory of network motifs – subgraphs that repeat themselves (patterns) among various networks. System engineering has traditionally followed a top-down methodology which creates a framework for the system and adds additional features to meet specific design requirements. Meanwhile, complex swarm systems, such as ant colonies and bird flocks, are formed via a bottom-up manner where the system-level structure directly emerges from the interactions and behaviors among individuals. The behaviors of these individuals cannot be directly controlled, which makes the engineering of these systems to be quite difficult. In recent studies, the discovery of network motifs has presented the ability to determine reoccurring similarities between similar functioning networks that were originally believed to have not shared any characteristics. Based on this research, we hypothesized that manipulating the combination of network motifs can engineer artificial swarms with improved functionality. In this study, we will model complex swarm systems as complex networks where each node represents an individual entity while links represent the communications between entities. Furthermore, we will use motif-detecting algorithms to search for subgraphs that reoccur in these complex networks. In collaboration with researchers from the University of Illinois at Chicago, preliminary research conducted at the University of Arkansas has shown promising results that reveal a potential correlation between network motifs and the performance of simulated swarm networks. Further research will look to verify these discovered correlations and develop methods to engineer improved swarm networks via network motifs.


artificial swarm design, swarm engineering, complex networks, network motifs, simulation study, service learning (26454 kB)