BEECLUST swarm algorithm: analysis and implementation using a Markov chain model Online publication date: Thu, 31-Jul-2014
by James Hereford
International Journal of Innovative Computing and Applications (IJICA), Vol. 5, No. 2, 2013
Abstract: In this paper, we analyse a new swarm search algorithm based on the behaviour of social insects, specifically honey bees. The new algorithm, called BEECLUST, is unique because it does not require any bot-bot communication and does not require the bots to know their position. In this paper, we describe the BEECLUST algorithm and model the algorithm using a birth and death Markov chain. From the Markov model, we answer two questions: 1) Will the bots (eventually) congregate or cluster near the targets (maxima) in the search space? 2) How long does it take the bots to cluster? We corroborate the time-to-cluster analysis with laboratory measurements. The experimental results were done with epuck robots in two different search spaces. In general, the BEECLUST algorithm shows promise for using very simple bots in swarm search applications if conditions during the search allow for bot-bot collisions near the target.
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