

Monday, August 1, 2011

Swarm Intelligence

World of Computing

Swarm Intelligence (SI) is a kind of artificial intelligence that aims to simulate the behavior of swarms or social insects. Swarm refers to any loosely structured collection of interacting agents. Technically swarms are regarded as decentralized self-organized systems. Swarm intelligence has a multidisciplinary character its study provides insights that can help humans manage complex systems. There is no clear definition for swarm intelligence. Emergent behaviour, self-organized behaviour and collective intelligence are the related terms. Surprisingly swarm intelligence system has the ability to act in a coordinated way without any coordinator or external controller.
Swarm Intelligence of Ants

Collective intelligence is the key. A single ant, for example, is not that smart but a colony of ants is. As colonies, ants respond quickly and effectively to their evironment. They find shorted path to the best food source, allocate workers to different tasks, and defend their terroritory from enemies. Ant colonies make these possible by countless interractions between individual ants. Each ant follows a simple rule of thumb. Each ant acts only on local information. A system that exhibits this behavior is said to be self-organizing. And the intelligence the ants exhibit collectively is called swarm intelligence.

Marco Dorigo, at the Université Libre in Brussels, used swarm intellgence in 1991 to create mathematical procedures for solving complex problems, such as routing trucks, scheduling airlines, or guiding military robots.

Swarm Intelligence of Honey Bees

Honey bees also exhibit swarm intelligence. Thomas Seeley, a biologist at Cornell University, has found the ability of honeybees to make good decisions. With as many as 50,000 workers in a single hive, honeybees have evolved ways to work to do what’s best for the colony. Honey bees use an odor for conveying information.

Applications of Swarm Intelligence

Beckers et al. (1994) have programmed a group of robots to implement clustering behavior of ants. This is one of the first swarm intelligence scientific oriented studies in which artificial agents were used.

A number of swarm intelligence studies have been performed with swarms of robots for validating mathematical models of biological systems.

In a now classic experiment done in 1990, Deneubourg and his group showed that, when given the choice between two paths of different length joining the nest to a food source, a colony of ants has a high probability to collectively choose the shorter one. Deneubourg has shown that this behavior can be explained via a simple probabilistic model in which each ant decides where to go by taking random decisions based on the intensity of pheromone perceived on the ground, the pheromone being deposited by the ants while moving from the nest to the food source and back.

Some of the Swarm-Inspired Methods are
Ant colony optimization – ACO
Particle swarm optimization – PSO

For Further Study

Mark Fleischer, Foundations of Swarm Intelligence: From Principles to Practice, 2005.