ABSTRACT
Originating from biology, a swarm represents a large group of insects or small organisms, particularly in motion. Such a group tends to organize
itself in order to solve a common problem, like searching a source of food, building nests, crossing obstacles, hunting, or defending against an overwhelming threat. In spite of the fact that swarmers usually are not very powerful individuals, by using relatively simple rules of actions, communication and local interactions, they manage to build up some kind of collective decentralized intelligence (Bonabeau 2003), capable to solve complex problems. Some examples of swarming in nature can be found in (Parunak and Brueckner 2004), related to ants, bees, fi shes, birds, wolves, etc. Such collective behaviour attracted attention not only of biologists, but also of scientists, engineers and experts from many other areas of contemporary human activities, who have been involved in research for the last three decades to fi nd new methods and algorithms inspired by swarming in bio systems. A suitable defi nition of swarming, suffi ciently general to include both biological and artifi cial systems, has also been given in the same text: “Swarming is useful self-organization of multiple entities through local interactions”.
