Four Classes of Morphogenetic Collective Systems
This work addresses the problem of designing and analyzing morphogenetic collective systems for researchers in swarm intelligence and self-organization, but it is incremental as it builds on existing models.
The study tackled the problem of understanding how morphogenetic principles influence self-organization in collective systems by defining four classes and conducting simulations, revealing that heterogeneity strongly impacts swarm structure while dynamic differentiation and local information sharing help maintain coherent organization.
We studied the roles of morphogenetic principles---heterogeneity of components, dynamic differentiation/re-differentiation of components, and local information sharing among components---in the self-organization of morphogenetic collective systems. By incrementally introducing these principles to collectives, we defined four distinct classes of morphogenetic collective systems. Monte Carlo simulations were conducted using an extended version of the Swarm Chemistry model that was equipped with dynamic differentiation/re-differentiation and local information sharing capabilities. Self-organization of swarms was characterized by several kinetic and topological measurements, the latter of which were facilitated by a newly developed network-based method. Results of simulations revealed that, while heterogeneity of components had a strong impact on the structure and behavior of the swarms, dynamic differentiation/re-differentiation of components and local information sharing helped the swarms maintain spatially adjacent, coherent organization.