Swarm intelligence (SI) is an artificial intelligence technique based around the study of collective behaviour in decentralised, self-organised, systems. The expression "swarm intelligence" was introduced by Beni & Wang in 1989, in the context of cellular robotic systems (see also cellular automata).

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 centralised control structure dictating how individual agents should behave, local interactions between such agents often lead to the emergence of global behaviour. Examples of systems like this can be found in nature, including ant colonies, bird flocking, animal herding, bacteria molding and fish schooling.

The probably most successful swarm intelligence techniques developed up to now are Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). ACO is a metaheuristic (other examples of metaheuristics are simulated annealing, tabu search, evolutionary computation, and so on) 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. PSO is a global minimisation technique 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 minimisation 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.

Swarm robotics is the application of swarm intelligence principles to large numbers of cheap robots. A particularly interesting application of swarm robotics principles can be found in the SWARM-BOTS project.

Researchers

• Carl Anderson
• Payman Arabshahi
• Eric Bonabeau
• Maurice Clerc
• Marco Dorigo
• Russell C. Eberhart
• Luca Gambardella
• Paul Kantor
• James Kennedy
• Marco Mamei
• Alcherio Martinoli
• Rui Mendes
• Ronaldo Menezes
• Yuhui Shi
• Guy Theraulaz
• Robert Tolksdorf
• Franco Zambonelli
• Paolo Gaudiano

References

• Swarm Intelligence by James Kennedy and Russell C. Eberhart. ISBN 1558605959
• Swarm Intelligence: From Natural to Artificial Systems by Eric Bonabeau, Marco Dorigo and Guy Theraulaz. ISBN 0195131592
• Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds by Mitchel Resnick. ISBN 0262181622

External Links

• Swarm Intelligence Resources
• NetLogo, a free software for multi-agent modeling, simulation, and the like, which can be used to explore the concepts of swarm intelligence.
• Cool School, free software that demonstrates fish flocking
• Swarm-Bots Project
• Particle Swarm Central
• Particle Swarm Optimization toolbox An open source pso toolbox written in MATLAB. (sourceforge home for the project)