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Swarm intelligence (SI) describes the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.

SI systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.

The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems.

Example algorithms

Ant Colony Optimization

Ant colony optimization (ACO) is a class of optimization algorithms modeled on the actions of an ant colony. ACO methods are useful in problems that need to find paths to goals. Artificial 'ants' - simulation agents - locate optimal solutions by moving through a parameter space representing all possible solutions. Real ants lay down pheromones directing each other to resources while exploring their environment. The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions.One variation on this approach is the bees algorithm, which is more analogous to the foraging patterns of the honey bee.

Particle Swarm Optimization

Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each timestep. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.

Stochastic Diffusion Search

Stochastic Diffusion Search (SDS) is an agent-based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions. Each agent maintains a hypothesis which is iteratively tested by evaluating a randomly selected partial objective function parameterised by the agent's current hypothesis. In the standard version of SDS such partial function evaluations are binary, resulting in each agent becoming active or inactive. Information on hypotheses is diffused across the population via inter-agent communication. Unlike the stigmergic communication used in ACO, in SDS agents communicate hypotheses via a one-to-one communication strategy analogous to the tandem running procedure observed in some species of ant. A positive feedback mechanism ensures that, over time, a population of agents stabilise around the global-best solution. SDS is both an efficient and robust search and optimisation algorithm, which has been extensively mathematically described.


Swarm Intelligence-based techniques can be used in a number of applications. The U.S. military is investigating swarm techniques for controlling unmanned vehicles. The European Space Agencymarker is thinking about an orbital swarm for self assembly and interferometry. NASAmarker is investigating the use of swarm technology for planetary mapping. A 1992 paper by M. Anthony Lewis and George A. Bekey discusses the possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors.

Crowd simulation

Artists are using swarm technology as a means of creating complex interactive systems or simulating crowds. Tim Burton's Batman Returns was the first movie to make use of swarm technology for rendering, realistically depicting the movements of a group of bats using the Boids system. The Lord of the Rings film trilogy made use of similar technology, known as Massive, during battle scenes. Swarm technology is particularly attractive because it is cheap, robust, and simple.

Ant-based routing

The use of Swarm Intelligence in Telecommunication Networks has also been researched, in the form of Ant Based Routing. This was pioneered separately by Dorigo et al. and Hewlett Packard in the mid-1990s, with a number of variations since. Basically this uses a probabilistic routing table rewarding/reinforcing the route successfully traversed by each "ant" (a small control packet) which flood the network. Reinforcement of the route in the forwards, reverse direction and both simultaneously have been researched: backwards reinforcement requires a symmetric network and couples the two directions together; forwards reinforcement rewards a route before the outcome is known (but then you pay for the cinema before you know how good the film is). As the system behaves stochastically and is therefore lacking repeatability, there are large hurdles to commercial deployment. Mobile media and new technologies have the potential to change the threshold for collective action due to swarm intelligence (Rheingold: 2002, P175).

References in popular culture

Swarm intelligence-related concepts and references can be found throughout popular culture, frequently as some form of collective intelligence or group mind involving far more agents than used in current applications.

  • The Borg in Star Trek
  • Science fiction writer Olaf Stapledon may have been the first to discuss swarm intelligences equal or superior to humanity. In Last and First Men (1931), a swarm intelligence from Mars consists of tiny individual cells that communicate with each other by radio waves; in Star Maker (1937) swarm intelligences founded numerous civilizations, composed of flocks of birds or, as the universe cools, energy-efficient swarms of burrowing slugs; ultimately all intelligence in the universe swarms into a single entity barely capable of perceiving the Supreme Moment.
  • Prey, by Michael Crichton deals with the danger of nanobots escaping from human control and developing a swarm intelligence.
  • Wyrm, a novel by Mark Fabi deals with a virus developing emergent intelligence on the Internet
  • Swarm, a short story by Bruce Sterling about a mission undertaken by a faction of humans, to understand and exploit a space-faring swarm intelligence.
  • The Hacker and the Ants, a book by Rudy Rucker on AI ants within a virtual environment
  • Ygramul, the Many - an intelligent being consisting of a swarm of many wasp-like insects, a character in the novel The Neverending Story written by Michael Ende. Ygramul is also mentioned in a scientific paper Flocks, Herds, and Schools written by Knut Hartmann (Computer Graphics and Interactive Systems Otto-von-Guericke-University of Magdeburg).
  • Allucination, a short story by Isaac Asimov about an alien insect-like swarm, capable of organization and provided with a sort of swarm intelligence.
  • The Invincible science fiction novel by Stanislaw Lem where a human spaceship finds an intelligent behavior in a flock of small particles that were able to defend themselves against what they found as a menace.
  • In The Matrix movies, the robotic sentinels exhibit signs of swarm intelligence. Additionally, in The Matrix Revolutions, a machine called the Deus Ex Machina uses a swarm of thousands of insect-like robots to form a giant animated face.
  • In the anime, Soukou no Strain, unmanned robotic drones known as TUMORS display signs of swarm intelligence as they attack in groups.
  • In the dramatic novel and subsequent mini-series: "The Andromeda Strain" by author Michael Crichton, A virus from the future communicates between individual cells and displays the ability to think and react individually and as a whole, and as such displays a semblance of "swarm intelligence".
  • In the computer RPG Planescape: Torment, the protagonist encounters monsters called cranium rats, which grow more intelligent in larger groups. The game features encounters with individual rats, small groups, and a very large group that identifies itself as "Many-as-One".
  • The videogame Mass Effect the Quarians created a race of humanoid sentient machines known as the Geth. Geth are said to possess limited artificial intelligence and problem solving skills when alone, but automatically network with each other when in groups. This means that the machine's ability to solve problems increases in correlation with the size of the group.
  • The television series Stargate SG-1 has entities know was replicators. Individually they only existed for their own survival, consuming and avoiding danger. However they also were able to link up all other replicators in the area. This made it possible like the Geth in Mass Effect to have vastly superior intelligence than would be expected from such a basic entity. This allowed them to defeat opponents that individually dwarfed them by a large extent in terms of intellect and size due to cluster computing.
  • In the Commonwealth Saga by Peter F. Hamilton, the Dyson Aliens exhibit elements of swarm intelligence with their linked immotile brains and motile ancillary units.

Notable Researchers

See also


  1. Beni, G., Wang, J. Swarm Intelligence in Cellular Robotic Systems, Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30 (1989)
  2. Ant Colony Optimization by Marco Dorigo and Thomas Stützle, MIT Press, 2004. ISBN 0-262-04219-3
  3. Recent Approaches to Global Optimization Problems Through Particle Swarm Optimization, by Parsopoulos, K.E., Vrahatis, M.N., Natural Computing, 1 (2-3), pp. 235-306, 2002.
  4. Particle Swarm Optimization by Maurice Clerc, ISTE, ISBN 1-905209-04-5, 2006.
  5. Lewis, M. Anthony, and Bekey, George A. The Behavioral Self-Organization of Nanorobots Using Local Rules. Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems.
Rhiengold, Howard (2002) Smart Mobs: The next social revolution, Perseus, Cambridge, Massachusetts


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