Multimedia applications requires QoS based routing methodologies.
Routing is an NP Hard Problem. Hence Artificial Intelligence
techniques will be required to solve such problems. Ant Colony
Optimization can be used to enhance routing using multiple QoS
parameters. ACO is a probabilistic technique for solving
computational problems which can be reduced to finding good paths
through graphs. Each ant is an autonomous agent that constructs a
path. There might be one or more ants concurrently active at the
same time. Ants do not need synchronization. Forward ants move to
good looking neighbor from the current node probabilistically.
Probabilistic choice is biased by pheromone trails previously
deposited and heuristic function. Without heuristics information,
algorithms tend to converge towards initial random solution. In
backward mode, ants lay down pheromone. In ACO pheromone is added
only to arcs belonging to the global best solution. Pheromone
intensity of all the paths decreases with time, called pheromone
evaporation. It helps in unlearning poor quality solution. After
some time, the shortest path has the highest probability.
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