Toward the Design of Artificial Swarms Using Network Motifs

Many complex systems evolve as a result of interactions among individual entities whose behaviors cannot be directly controlled. This makes the design of such systems inherently challenging. 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 among various networks. 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. It is therefore hypothesized that manipulating the types of network motifs within a network can help engineer artificial swarms with improved functionality. In this study, artificial swarm systems have been modeled as a dynamic complex network where each node represents an individual foraging entity and links represent as the communication between entities. Additionally, motif-detecting algorithms have been used to extract subgraphs that reoccur in these complex networks. Our research has shown promising results that reveal a statistically significant correlation between network motifs and the performance of simulated swarm networks. This study contributes as a new approach that can potentially be used in the design and engineering of complex swarm systems.