Fractals everywhere
Introduction to distributed algorithms
Introduction to distributed algorithms
Iterative solution methods
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Stability analysis of social foraging swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Coordination and control of multi-agent dynamic systems: models and approaches
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
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We consider swarms as systems with partial random synchronicity and look at the conditions for their convergence to a fixed point. The conditions turn out to be not much more stringent than for linear, one-step, stationary iterative schemes, either synchronous or sequential. The rate of convergence is also comparable. The main result is that swarms converge in cases when synchronous and/or sequential updating systems do not. The other significant result is that swarms can undergo a transition from non convergence to convergence as their degree of partial synchronicity diminishes, i.e., as they get more “disordered”. The production of order by disordered action appears as a basic characteristic of swarms.