Examples Of Swarm Intelligence

Abstract— The collective behavior of decentralized and self-organized systems in nature has become a very important topic now. Anything in group is said swarm. A large number of Simple Individuals who work in group have a special kind of intelligent behavior, it is called Swarm Intelligence. This is a collective behavior of the individuals who interacts with each other through the environment. The common examples of swarm intelligence are Ant colony, Bird flocks, Bees colony etc. This type of group work can be incorporated to build any artificial intelligent system which will be flexible and robust with direct or indirect interactions. In this paper we are discussing about some of the Swarm Intelligence models such as, Ant colony Optimization

Ants cannot communicate with each other directly. This type of communication is called “Stigmergy” (“Stigmergy” is a consensus social network mechanism of indirect coordination, through the environment, between agents or actions. The principle is that the trace left in the environment by an action stimulates the performance of a next action, by the same or a different agent.). First individual ant randomly wanders to search food source near their colony.
2. If the ants find their food source they immediately come back near the nest on its way back it leaves a chemical substances called as pheromone. These pheromones are volatile in nature they keep evaporating.
3. The other ants that are near the food source get attracted by the pheromones so they start to follow the same route. When they follow the route every time they leave pheromones so, the layer of pheromone gets thicker and, it becomes easier to find food source for the rest of the ants.
4. In the case of shortest path to the food source ants always follow the shortest path. If any ant find the shorter path than the first one the shortest route becomes more

In case if the ants find any obstacle while following their suitable route they scatter randomly at first but they again find the shortest path to reach the food source. Figure1. Figure 1: Ant’s stigmergic behavior in finding the shortest path between the food and nest.
If we concentrate on the behaviour of the ants we find two concept of the amazing group work:
1. Ants generating a positive feedback when they find the food which increases simultaneously when the number of ants passing the route increases.
2. Ants generating a positive feedback through the pheromone. In case the pheromone that is a chemical substance gets evaporated there is a high chance that the other ants can get a negative feedback or even no feedback at all. [14][15]
Algorithm of ACO model:
1. Initialization of ACO metaheuristics. (“A metaheuristic refers to a master strategy that guides and modifies other heuristics to produce solutions beyond those that are normally generated in a quest for local optimality” – Fred Glover and Manuel Laguna In simpler way: It is a set of algorithms used to define heuristic methods that can be used for a large set of problems)
2. Construct plans for solution
3. Update pheromone depending on positive or negative