Swarm intelligence refers to collective intelligence. Biologists and natural scientist have been studying the behavior of social insects due to their efficiency of solving complex problems such as finding the shortest path between their nest and food source or organizing their nests. In spite of the fact that these insects are unsophisticated individually, they make wonders as a swarm by interaction with each other and their environment. In last two decades, the behaviors of various swarms that are used in finding preys or mating are simulated into a numerical optimization technique. In this chapter, eight different swarm intelligence–based algorithms are summarized and their working steps are listed. These techniques are ant colony optimizer, particle swarm optimizer, artificial bee colony algorithm, glowworm algorithm, firefly algorithm, cuckoo search algorithm, bat algorithm, and hunting search algorithm. Two optimization problems taken from the literature are solved by all these eight algorithms and their performance are compared. It is noticed that most of the swarm intelligence–based algorithms are simple and robust techniques that determine the optimum solution of optimization problems efficiently without requiring much of a mathematical struggling.

However, most ants do not separate flow into specific lanes like the army ants. Instead they rely on other traffic-control rules. For the European black garden ant, Lasius niger, one path is used at low densities where collision-avoidance maneuvers of the individual ants end up avoiding the formation of traffic jams. However, as the density of workers in the path builds up, a new separate path is formed before the density gets so high that significant delays from traffic jams can occur Again, this bifurcation arises as a result of the behavioral rules of the individual worker ants in that at higher densities ants start to push oncoming ants onto another path.

Swarm-intelligence principles inspired by the collective insect societies are used for developing computer algorithms and motion control principles for robotics. The basic idea is that a swarm of individuals can coordinate and behave as a single entity that performs better than the individuals. Using cooperative behavior, the individuals help each other and solve problems that cannot be handled by the single individuals. Such a collection of cooperating robots is referred to as a swarm-botBecause the individual robots in the swarm can communicate with simple sensors such as light and sound, they stay together and avoid barriers such as walls and holes

Nouyan and  performed an experiment with up to 12 small physical robots that were given the foraging task to move a prey to a home position called the nest. Movement of the prey required concurrent physical handling from more than one robot. The single robots physically attached to each other in order to pull the prey. The robots only had a small perceptual range and would therefore not be able to find their way back to the nest on their own. They relied on contact with other robots that were within their range. In most of their experiments, where the group size was sufficiently large, the robot swarm group succeeded in retrieving the prey to the nest.

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International journal of swarm intelligence and evolutionary computation
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