Abstract
Swarm intelligence deals with the behavior of natural or artificial swarms. Swarms are systems that consist of many individuals that are organized and coordinated by principles of decentralized control, indirect communication, and self-organization. Examples of natural swarms are social insect colonies, flocks of birds, schools of fish, or herds of land animals. Examples of artificial swarms include groups of robots, intelligent mobile devices that can communicate with each other, or virtual swarms in form of a computer program. An interesting phenomenon of swarms is that collective swarm behavior can emerge on a global scale even when all individuals have only a restricted view of the system and interactions between individuals and their environment occur only on a local scale. Examples for such collective behavior are the nest building of ants and termites or the coordinated movement of a fish school in reaction to the occurrence of prey. In this article we focus on the optimization methods that have been developed in the field of swarm intelligence.
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