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

ABSTRACT

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.

Graphical Abstract

Four different types of individual behaviors work together to increase the performance of decision making in the swarm.

KEYWORDS:

• Autonomous agents
• self-organisation
• behavioural patterns
• honeybees
• search
• swarm systems

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported mainly by: Austrian Science Fund (FWF): [grant number P19478-B16], [grant number P23943-N13] (REBODIMENT); EU FP7 FET-Proactive ‘ASSISI${}_{\mathit{bf}}$’, [grant number 601074]; Author P.Z is supported by EU-H2020 project ‘florarobotica’, [grant number 640959].