| Amorphous
Computing and Swarm
Intelligence |
Introduction Amorphous
computing consists of a multitude of interacting computers with modest computing
power and memory, and modules for intercommunication. These collections of devices
are known as swarms. The desired coherent global behaviour of the computer is
achieved from the local interactions between the individual agents. The global
behaviour of these vast numbers of unreliable agents is resilient to a small fraction
of misbehaving agents and noisy and intimidating environment. This makes them
highly useful for sensor networks, MEMS, internet nodes, etc. Presently, of the
8 billion computational units existing worldwide, only 2% of them are stand-alone
computers. This proportion is projected to further decrease with the paradigm
shift to the biologically inspired amorphous computing model. An insight into
amorphous and swarm computing will be given in this paper. The
ideas for amorphous computing have been derived from swarm behaviour of social
organisms like the ants, bees and bacteria. Recently, biologists and computer
scientists studying artificial life have modelled biological swarms to understand
how such social animals interact, achieve goals and evolve. A certain level of
intelligence,exceeding those of the individual agents, results from the swarm
behaviour. Amorphous Computing is a established with a collection of computing
particles -with modest memory and computing power- spread out over a geographical
space and running identical programs. Swarm Intelligence may be derived from the
randomness, repulsion and unpredictability of the agents, thereby resulting in
diverse solutions to the problem. There are no known criteria to evaluate swarm
intelligence performance. Inspiration
The
development of swarm computing has been instilled by some of the natural phenomenon.
The
most complex of the activities, like optimal path finding, have been executed
by simple organisms. Lately MEMS research has paved the way for manufacturing
the swarm agents with low costs and high efficiency.
The
biological world
In case of
the ant colonies, the worker ants have decentralised control and a robust mechanism
for some of the complex activities like foraging, finding the shortest path to
food source and back home, build and protect nests and finding the richest food
source in the locality. The ants communicate by using pheromones. Trails of pheromone
are laid down by a given ant, which can be followed by other ants. Depending on
the species, ants lay trails travelling from the nest, to the nest or possibly
in both directions. Pheromones evaporate over time. Pheromones also accumulate
with multiple ants using the same path. As the ants forage, the optimal path to
food is likely to have the highest deposition of pheromones, as more number of
ants follow this path and deposit pheromones. The longer paths are less likely
to be travelled and therefore have only a smaller concentration of pheromones.
With time, most of the ants follow the optimal path. When the food sources deplete,
the pheromones evaporate and new trails can be discovered. This optimal path finding
approach has a highly dynamic and robust nature.
Similar
organization and behaviour are also present in the flocks of bird. For a bird
to participate in a flock, it only adjusts its movements to coordinate with the
movements of its flock mates, typically its neighbours that are close to it in
the flock. A bird in a flock simply tries to stay close to its neighbours, but
avoid collisions with them. Each bird does not take commands from any leader bird
since there is no lead bird. Any bird can °y in the front, center and back
of the swarm. Swarm behaviour helps birds take advantage of several things including
protection from predators (especially for birds in the middle of the flock), and
searching for food (essentially each bird is exploiting the eyes of every other
bird). Even complex biological entities like brain are a swarm of interacting
simple agents like the neurons. Each neuron does not have the holistic picture,
but processes simple elements through its interaction with few other neurons and
paves way for the thinking process.
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