Towards the Minimization of Cyclic Instability Using Embedded Algorithms

In recent years, the problem of cyclic instability has been investigated mainly using two approaches: analysing the topological properties of the system (finding loops or feedback) and bio-inspired optimization. One of the main disadvantages of analysing the topology of the system (i.e. the connectivity of the agents involved in the environment) is the computational cost (that could be increased if the environment includes nomadic agents). Optimization-based approaches have been proven to work very well, even in the case of nomadic agents. However, the optimisation approach has been deployed mainly using computer simulations. With the breakthrough of integrated circuits, allowing a wide variety of low cost microcontrollers, the possibility of implementing intelligent algorithms (such as fuzzy logic, neural networks, etc.) on embedded agents is a reality. In this paper, we present a preliminary analysis toward the implementation of bio-inspired optimisation algorithms on embedded systems. Our long-term goal is to be able to prevent cyclic instability in real and complex rule based multi-agent environments using optimisation algorithms on embedded system.