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Swarm Intelligence


Published on Nov 21, 2015

Abstract

Nature is a great place to go for inspiration when you want to see systems that are robust and have been around for millions of years. Nature provides the inspiration for swarm intelligence. Look at the emergent behavior observed in ants, termites, bees and others. We see very simple creatures, performing complex behavior as a group

Consider the case of ant colony working together .

The behaviour of ants has long fascinated scientists. These insects have the strength to carry food up to seven times their own body weight, and set up amazingly complex colonies, with social 'castes' in which every member has a role.
In fact, ants are not only fascinating just to entomologists looking at them under the microscope. In recent years, computer scientists have been paying great attention to the way in which a colony of ants can solve complex problems; in particular, how it finds the shortest route to a food source.

Each insect in a colony seemed to have its own agenda, and yet the group as a whole appeared to be highly organized. This organization was not achieved under supervision, but through interaction among individuals. This was most apparent in the way in which ants travel to and from a food source.

Ants form and maintain a line to their food source by laying a trail of pheromone, i.e. a chemical to which other members of the same species are very sensitive. They deposit a certain amount of pheromone while walking, and each ant prefers to follow a direction rich in pheromone. This enables the ant colony to quickly find the shortest route. The first ants to return should normally be those on the shortest route, so this will be the first to be doubly marked by pheromone (once in each direction). Thus other ants will be more attracted to this route than to longer ones not yet doubly marked, which means it will become even more strongly marked with pheromone.

Thus, the shortest route is doubly marked, and more ants will follow it. This simple model finds the shortest route between the nest and a food source. Allowing the pheromone trail to "evaporate" (as in nature) provides the ants a mechanism to explore for alternate food sources when the first is depleting and for alternate routes should the first become blocked. Studying this uncanny skill has enabled researchers to create software agents capable of solving complex IT problems.This forms the basic idea behind SWARM INTELLIGENCE

CHARATERISTICS OF SWARM

" Distributed, no central control or data source;

" No (explicit) model of the environment;

" Perception of environment, I.e. sensing;

" Ability to change environment

TRAVELING SALES ANT

In the traveling salesman problem,a person must find the shortest route by which to vcisit a given number of cities,each exactly once.The classic problem is devilishly difficult:for just 15 cities there are billions of route possiblities.

Recently researchers have begun to experiment with antlike agents to derive a solution.The approach relies on the artificial ants laying and following equivalent of pheromone trails














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