Its quite fashionable to ask nowadays why one should follow the conventional rules of life. These rules about our education system, marriage, career are often being much criticized and not many have answers for it. Many follow them blindly and some question them, follow their own track and get trapped. Quite a few succeed in their ways too. I do not justify following the conventional rules blindly but there has to be a reason for people advising to follow it and people who follow it. By applying swarm intelligence theory to this phenomenon I believe I have an answer to it. By theory its the optimal route to follow or else risk it with 10% confidence levels.
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Swarm intelligence (SI) is artificial intelligence based on the collective behavior of decentralized, self-organized systems. The expression was introduced by Gerardo Beni and Jing Wangin 1989, in the context of cellular robotic systems. Basically it describes the intelligence exhibited by creatures as a group what they would not be able to do independently.
SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local interactions between such agents lead to the emergence of complex global behavior. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.
Behavior of Ant Colonies
In the real world, ants (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails. If other ants find such a path, they are likely not to keep traveling at random, but to instead follow the trail, returning and reinforcing it if they eventually find food.
Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength. The more time it takes for an ant to travel down the path and back again, the more time the pheromones have to evaporate. A short path, by comparison, gets marched over faster, and thus the pheromone density remains high as it is laid on the path as fast as it can evaporate. Pheromone evaporation has also the advantage of avoiding the convergence to a locally optimal solution. If there were no evaporation at all, the paths chosen by the first ants would tend to be excessively attractive to the following ones. In that case, the exploration of the solution space would be constrained.
Thus, when one ant finds a good (i.e. short) path from the colony to a food source, other ants are more likely to follow that path, and positive feedback eventually leads all the ants following a single path. The idea of the ant colony algorithm is to mimic this behavior with “simulated ants” walking around the graph representing the problem to solve.
Reference: Wikipedia
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