Swarm Intelligence

OVERVIEW
Swarm intelligence is defined as the collective behavior of fragmentized, self-established groups. They are made up of simple agents that interact with the environment (so called stigmergy) and between each other. The agents follow simple rules and possess themselves limited capabilities. They don’t follow centralized orders for each individual and interact locally and randomly but together, from a global point of view, their behaviour emerges as ”intelligent”

Examples for such actions are hunting for food by ants, or hunting for nectar by bees. The nature of swarms largely looks like mobile ad-hoc networks (MANETs) and that is the reason ideas from swarm animals like ants and bees are used for creating suitable routing protocols

1.1 All ants take the shortest path after an initial searching time

Fig1.1 shows a scenario in which the best route between two choices is chosen by the ants after a while. Bee-inspired algorithms are mainly based on the foraging principle of honey bees.
Swarm intelligence is an area of research that over the last decenary has experienced a bang in interest. Inspired by the manifestly intelligent behavior of swarms of primary animals, swarm intelligence has insistent to be a budding field of research in several different areas.

A swarm of ants in the search for food shows the astounding capability of finding shortest paths between a found food source and the colony. Even though any single ant could be said to have the capability of finding a short path from the colony to a nearby food source, the probability of this happening is very less, since an ant is not a very intelligent animal. The remarkable thing is that when many ants collaborate on searching food, using pheromone analysis as a simple indirect form of communication, the swarm of ants could to be able to find a shortest path productively. Another feature is their ability to habituate to a changing environment. If an obstacle is found on the path from the food source back to the anthill, ants are efficient of finding the shortest path around the obstacle – and probably find food sources closer to the

Different from general transmission methods, it has two distinct characteristics: First, by considering energy consumption in different areas, it achieves not only Maximum Possible Energy Efficiency MPEE and also Maximum Possible Energy Balancing MPEB, and finally prolongs the network longevity. Second, it not only considers MPEB, but also advances two inter-related strategies of MPEB. The UMM algorithm has a unique proposition that improves the longetivity of the network and also takes cares that the number of alive nodes are more as compared to other nodes in the