Towards swarm level optimisation: the role of different movement patterns in swarm systems.

In a swarm system, for example in a beehive, group decision is based on interactions and interferences of all individuals without a central unit that decides for everybody. When making experiments with young honeybees (Apis mellifera), a swarm algorithm, called BEECLUST, was derived. The algorithm enables swarms to locate the ‘Global-Goal’ out of several local optima. There were also four different behavioural types discovered during the experiments: Random-Walker, Goal-Finder, Wall-Follower and the Immobile Bee. In this paper, we introduce the four behavioural types to the BEECLUST algorithm and analyse how the decision making process of the swarm can be influenced. We show how the different types can be used to optimise the decision making for a certain setup of the arena and discuss about Swarm Level Optimisation.