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Swarm intelligence (SI) is an artificial intelligence technique based around the study of collective behavior in decentralized, self-organized systems. The expression "swarm intelligence" was introduced by Beni & Wang in 1989, in the context of cellular robotic systems.

SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. Although there is normally no centralized control structure dictating how individual agents should behave, local interactions between such agents often lead to the emergence of global behavior. Examples of systems like this can be found in nature, including ant colonies, bird flocking, animal herding, bacteria molding and fish schooling.

Application of swarm principles to large numbers of robots is called as swarm robotics.

Example systems

Ant colony optimization

Ant colony optimization or ACO is a metaheuristic optimization algorithm that can be used to find approximate solutions to difficult combinatorial optimization problems. In ACO artificial ants build solutions by moving on the problem graph and they, mimicking real ants, deposit artificial pheromone on the graph in such a way that future artificial ants can build better solutions. ACO has been successfully applied to an impressive number of optimization problems.

Particle swarm optimization

Particle swarm optimization or PSO is an agent based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into many simpler functions. Unlike the stigmergetic 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 stabilize around the global-best solution. SDS is both an efficient and robust search and optimization algorithm, which has been extensively mathematically described.

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

Description needed

Applications

Swarm Intelligence-based techniques can be used in a number of applications. The U.S. military is investigating swarm techniques for controlling unmanned vehicles. NASA 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. Artists are using swarm technology as a means of creating complex interactive environments. Disney's The Lion King was the first movie to make use of swarm technology (the stampede of the bisons scene). The movie "Lord of the Rings" has also made use of similar technology during battle scenes. Swarm technology is particularly attractive because it is cheap, robust, and simple.

References in popular culture

Swarm Intelligence-related concepts and references can be found throughout popular culture:
  • Prey, by Michael Crichton deals with the danger of intelligent nano-robots escaping from human control and becoming dangerous.
  • Wyrm, a novel by Mark Fabi deals with a virus developing emergent intelligence on the Internet
  • Jason X, a movie in the Friday the 13th series, had a swarm of nanobots repair Jason's damaged body.
  • Hacker and the ants, a book by Rudy Rucker on AI ants within a virtual environment

Researchers

See also List of swarm researchers.

See also

References

  • Swarm Intelligence: From Natural to Artificial Systems by Eric Bonabeau, Marco Dorigo and Guy Theraulaz. (1999) ISBN 0195131592
  • Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds by Mitchel Resnick. ISBN 0262181622
  • Swarm Intelligence by James Kennedy and Russell C. Eberhart. ISBN 1558605959
  • The Behavioral Self-Organization of Nanorobots Using Local Rules. by Lewis, M. Anthony, and Bekey, George A. (1992) Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems.
  • Fundamentals of Computational Swarm Intelligence by Andries Engelbrecht. Wiley & Sons. ISBN 0470091916
  • 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.
  • Ant Colony Optimization by Marco Dorigo and Thomas Stützle, MIT Press, 2004. ISBN 0262042193
  • Particle Swarm Optimization by Maurice Clerc, ISTE, ISBN 1905209045, 2006.

External links

Swarm simulation links

Artificial intelligence | Optimization algorithms

Schwarmintelligenz | هوش ازدحامی | 群知能 | Intelligence collective

 

This article is licensed under the GNU Free Documentation License. It uses material from the "Swarm intelligence".

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