We have just back from the International Congress on Complex Systems, held in Agadir, Morocco, were we have presented the papers «Swarm Art with KANTS» and «Towards a 2-dimensional Self-organized Framework for Structured Population-based Metaheuristics». The first one continues our line of work with the KANTS algorithm as a swarm art tool and describes drawings generated by data extracted from photographs.
Original photos and respective pherogenic drawings by the KANTS algorithm
The second paper, which opens a new line of research, describes a swarm system that, guided by simple rules and with no central coordination, is driven to a state in which global patterns emerge. In that state, the components of the swarm self-organize into highly dynamic clusters. We show that the system is unpredictable and robust. We also demonstrate that the system’s variables (averaged clustering degree and averaged distance between neighboring components) dysplay 1/f noise. The abstract:
This paper proposes a swarm intelligence framework for distributed population-based metaheuristics that uses stigmergy and similarity measures as basic modeling rules with a local range of action for structuring the neighborhood. The system – which can be described as a cellular automaton with short-term memory – displays complex and emergent behavior whose most visible trait is the self-organization of a population of particles into dynamic clusters. These clusters tend to gather similar particles (similarity here is measured as the algebraic difference between randomly assigned fitness values). During the execution of the algorithm, the particles move through a grid of nodes leaving a mark with the fitness value of the particle in each node they visit. When deciding where to move, the particles take into account the marks in the neighborhood and tend to travel to nodes with marks that minimize the difference between the particle’s fitness and the mark’s fitness. A kind of hierarchical behavior is also modeled by forcing the particles to move toward nodes with better fitness values. We show that these simple rules conduct the system to a critical state in which clusters are constantly created and broken, while maintaining a typical pattern of clusters and paths. In addition, we demonstrate that the system’s variables display noise, which is one of the signatures of Self-Organized Criticality (SOC). Since it does not require the tuning of control parameters to precise values, we hypothesize that the proposed system converges to SOC.
The presentation:
GeNeura Team 